Machine learning, deep learning materials 1000+

Machine learning is the study of how the computer simulates or realizes the human learning behavior, in order to acquire new knowledge or skills, reorganize the existing knowledge structure to continuously improve its own performance. It is the core of artificial intelligence and the fundamental way to make computers intelligent. Deep learning is a new field of machine learning research, and it is also the field closest to AI. Its motivation is to build neural networks that simulate the analysis and learning of the human brain. It mimics the mechanism of the human brain to interpret data, such as images, sound and text.

Artificial intelligence is so hot, learn it!!

Deep learning

Deep Learning Popular Science Series:

How deep is Deep Learning? The confusion of Natural Language [1]

Deep learning, 2015^[2]

Top 10 Open Source Frameworks worth deep learning^[3]

Deep learning data collection for various programming languages^[4]

10 open source deep learning frameworks on GitHub^[5]

Machine learning:

Basic Concepts:

Simple and Easy to Learn Machine Learning Algorithms — Support Vector Machines^[6]

Pattern Recognition and machine learning: Some basic concepts in pattern recognition^[7]

Data Mining and Machine Learning ten graphs explain the basic concepts of machine learning^[8]

Gaussian Discriminant Analysis (GDA)^[9]

Stanford University: Machine Learning (Andrew Ng) — Naive Bayes^[10]

Machine Learning: Underfitting and Overfitting concepts^[11]

Machine learning 9 Reinforcement learning^[12]

There is supervised learning in machine learning — regression^[13]

Supervised Learning (machine learning)^[14]

Working platform:

Java machine learning software introduction^[15]

25 Java machine learning tools and libraries^[16]

Matlab machine learning algorithm function summary^[17]

Comparison of NLP tools and machine learning tools^[18]

Java machine learning resource collation^[19]

The following content is from Github[20], prepare the ladder, part of the article needs.

Brief History of Machine Learning^[21]

Introduction: This is an introduction to the history of machine Learning, introduction is very comprehensive, from perceptron, neural network, decision tree, SVM, Adaboost to random forest, Deep Learning.

Deep Learning in Neural Networks: An Overview^[22]

Introduction: This is the latest edition of “Neural Networks and Deep Learning Overview” written by Jurgen Schmidhuber of Swiss Artificial Intelligence Laboratory. This overview is featured by chronological order, starting from 1940, through the 1960s to the 1980s, the 1980s to the 1990s, and continues to the post-2000 and recent years. It covers all kinds of tricks in deep Learning, with very comprehensive references.

A Gentle Introduction to SciKit-Learn: A Python Machine Learning Library^[23]

This is a python machine learning library if you are a Python engineer and want to learn more about machine learning. Then this article may help you.

How to Layout and Manage Your Machine Learning Project^[24]

This article introduces how to design and manage your own machine learning project. It provides management templates, data management and practices.

Machine Learning is Fun!^[25]

Introduction: If you don’t know what machine learning is, or are just learning it and feel boring. Recommended reading then. This article has been translated into Chinese. If you are interested, you can go to blog.jobbole.com/67616/[26]

R Language Reference Card^[27]

R language is the main language of machine learning, there are a lot of friends want to learn R language, but always forget the meaning of some functions and keywords. Then this article may help you

Choosing a Machine Learning Classifier^[28]

Introduction: How do I choose machine learning algorithms? This paper intuitively compares the advantages and disadvantages of Naive Bayes, Logistic Regression, SVM, decision tree and other methods, and also discusses the issues of sample size, Feature and Model tradeoff. There is also a translated version :www.52ml.net/15063.html[29]

An Introduction to Deep Learning: From Perceptrons to Deep Networks^[30]

Introduction: Overview of deep learning: From perceptron to deep network, the author’s selection of examples and introduction of theories are in place, from simple to profound. Translation: www.cnblogs.com/xiaowanyer/… [31]

The LION Way: Machine Learning Plus Intelligent Optimization^[32]

Machine Learning and Optimization is a booklet on machine learning. It covers all aspects of machine learning in just over 300 pages. Well illustrated, vivid and easy to understand, without the trouble of a glop formula. Suitable for beginners to lay a foundation, also suitable for old hands to review the new. Maybe you need this one more than MLAPP/PRML! Specific content recommended reading: intelligent-optimization.org/LIONbook/ [33]
Deep Learning and Statistical Learning Theory^[34]

Introduction: The author is from Baidu, but he has applied for resignation in April 2014. But this is a good article if you don’t know how deep learning relates to support vector machines/statistical learning theory? Then you should read this article immediately.

Mathematics in Computer Science^[35]

Introduction: This book is published by Google and MIT, Mathematics for Computer Science[36], Eric Lehman et al 2013. It is divided into five parts: 1) proof and induction. 2) Structure, number theory, graph. 3) Counting, summing and generating functions. 4) Probability, random walk. 5) Recursion. , etc.

Foundations of Data Science in the Information Age^[37]

Introduction: Computer Science theory in the Information Age, paper book purchase, iTunes purchase at present [38]

Data Science with R^[39]

Introduction: This is the second edition of “Introduction to Data Science” textbook published by Syracuse University. It is practical and easy to understand, suitable for students who want to learn R language.

Twenty Questions for Donald Knuth^[40]

Introduction: This is not a document or a book. This is a transcript of a question to Donald Knuth, Turing Prize winner: Recently, Charles Leiserson, Al Aho, Jon Bentley and others asked Knuth 20 questions about TAOCP, P/NP problems, Turing machines, logic, and why the great Ones don’t use email.

Automatic Construction and Natural-language Description of Nonparametric Regression Models Automatic Construction and Natural-language Description of Nonparametric Regression Models^[41]

Introduction: How to do without statistics? Do not know how to choose the appropriate statistical model to do? It is important to read Joshua B. Tenenbaum of MIT and Zoubin Ghahramani of Cambridge, who wrote an article on Automatic Statisticians. Can automatically select regression model categories, can also automatically write reports…

ICLR 2014 Proceedings^[42]

Introduction: Students who are interested in the latest progress of deep learning and representation Learning can learn about it

Introduction to Information Retrieval^[43]

Introduction: This is a book about Information Retrieval, which is co-authored by Stanford Manning and Raghavan, vice president of Google, etc. It has always been one of the most popular Information Retrieval textbooks in North America. Recently the author has added slides and assignments for the course. IR related resources: www-nlp.stanford.edu/IR-book/inf… [44]

Machine Learning in 10 Pictures^[45]

Introduction :Deniz Yuret uses 10 beautiful diagrams to explain important concepts in machine learning: 1. Bias/Variance Tradeoff 2. Overfitting 3. Bayesian / Occam’s razor 4. Feature combination 5. Irrelevant feature 6. Basis function 7. Discriminative / Generative 8. Loss function 9. Least squares 10. Sparsity. Very clear

“Yahoo Research data set summary”^[46]

Introduction: Yahoo research institute’s data set summary: including language data, graph and social data, scoring and classification data, computing advertising data, image data, competition data, and system data.

An Introduction to Statistical Learning with Applications in R^[47]

Introduction: this is a famous professor at Stanford statistics Trevor Hastie and Robert Tibshirani’s new book, and in January 2014 has classes: class.stanford.edu/courses/Hum… [48]

Best Machine Learning Resources for Getting Started^[49]

Introduction: Machine learning best start learning materials summary is designed for machine learning beginners recommended quality learning resources, help beginners quickly start. And the introduction of this article has been translated into Chinese [50]. If you are not familiar with it, I suggest you read the Chinese introduction first.

My deep learning reading list^[51]

Introduction: Mainly following the article of Bengio’s PAMI Review. Including several review articles, nearly 100 papers, and presentations by all of you. All of them are available on Google.

Cross-Language Information Retrieval^[52]

Introduction: This is a book which mainly introduces the knowledge of cross-language information retrieval. The theory of a lot of

Exploring the inner secrets of recommendation engines, Part 1: A primer on recommendation engines^[53]

Introduction: This article is a three-part series written by an engineer from IBM. It mainly introduces the recommendation engine related algorithms, and helps readers to efficiently implement these algorithms. Exploring the secrets inside recommendation engines, Part 2: Deep recommendation engine correlation algorithms — collaborative filtering [54], Exploring the secrets inside recommendation engines, Part 3: Deep recommendation engine correlation algorithms — clustering [55]

Advice for Students of Machine Learning^[56]

Introduction: A Bit of Advice for Beginners in Machine Learning by David Mimno, assistant professor of information science at Cornell University, is a very practical book that emphasizes the combination of practice and theory, and concludes with a quote from Von Neumann: “Young man, in mathematics you don’t understand things. You just get used to them.”

Distributed parallel processing of data^[57]

Explorations in Parallel Distributed Processing A Handbook of Models, Programs, and Exercises, by James L. McClelland of Stanford. This paper focuses on the Distributed implementation of various god-level network algorithms, which can be used as a reference for the children of Distributed Deep Learning

What is “Machine Learning”?^[58]

Introduction: What is “machine learning”? John Platt is a distinguished scientist at Microsoft Research who has been working in machine learning for 17 years. Machine learning has become so popular in recent years that Platt and his colleagues decided to start a blog [59] to inform the public about the progress of machine learning research. What is machine learning and where is it being applied? Check out this post by Platt [60]

International Conference on Machine Learning (ICML) 2014^[61]

Introduction: The 2014 International Conference on Machine Learning (ICML) has been held in The National Convention Center from June 21 to 26. This conference, co-hosted by Microsoft Research Asia and Tsinghua University, is the first time for this world-renowned event in the field of machine learning with a history of more than 30 years to come to China, and has successfully attracted more than 1200 scholars from home and abroad to register and participate. A lot of dry goods, worth further study

Machine Learning for Industry: A Case Study^[62]

Introduction: This article mainly uses Learning to Rank as an example to illustrate the specific application of machine Learning in the business world. RankNet is insensitive to NDCG and becomes LambdaRank after NDCG is added. The same idea was changed from the neural network to the VANDA Tree model to make the LambdaMART. Chirs Burges[63], machine Learning guru of Microsoft, winner of Yahoo 2010 Learning to Rank Challenge, RankNet, LambdaRank, LambdaMART, In particular, LambdaMART is the most prominent, and the representative papers are: From RankNet to LambdaRank to LambdaMART: [64] In addition, Burges has many famous masterpieces, such as: A Tutorial on Support Vector Machines for Pattern Recognition[65] Some Notes on Applied Mathematics for Machine Learning[66]

100 Best GitHub: Deep Learning^[67]

100 Best GitHub: Deep Learning

“Deep Learning” by Andrew Ng, UFLDL^[68]

Introduction: This tutorial will illustrate the main ideas of unsupervised feature learning and deep learning. By learning, you will also implement multiple functional learning/deep learning algorithms, be able to see them work for you, and learn how to apply/adapt these ideas to new problems. This tutorial assumes the basics of machine learning (especially familiar with supervised learning, logistic regression, and the idea of gradient descent), and if you are not familiar with these ideas, we recommend that you go to the machine Learning course here [69] and first complete chapters II, III, and IV (to logistic regression). In addition, the source Code for this Tutorial is available in Python on Github with UFLDL Tutorial Code[70]

*《Deep Learning for Natural Language Processing and Related Applications》^[71]

Introduction: This document comes from Microsoft Research, and has a lot of essence. To be fully understood, some foundation in machine learning is required. But there are some things that will make you feel better.

Understanding Convolutions^[72]

Introduction: This is an introduction to the image convolution operation of the article, it has been calculated in detail

Machine Learning Summer School^[73]

Every day, we invite a big bull to lecture on machine learning, big data analysis, parallel computing and human brain research. www.youtube.com/user/smolix [74] (over the wall)

Awesome Machine Learning^[75]

Data mining for machine learning is a free ebook for data mining for machine learning [77]. Data mining for machine learning is a library for data mining for machine learning.

Stanford natural Language Processing^[78]

Introduction: All videos of the natural Language Processing course by Chris Manning, acL-president-elect and Professor of Computer Science at Stanford University, are now available on the Stanford Ocw web site (IE is not available if Chrome is not available) and the assignments and quizzes are also available for download.

Deep Learning and Shallow Learning^[79]

Chiyuan Zhang, a PhD student at MIT and a graduate of Zhejiang University, has written a blog about Deep Learning and Shallow Learning.

Spotify Music on Spotify with Deep Learning^[80]

Introduction: Music recommendation based on convolutional neural network.

Neural Networks and Deep Learning^[81]

Introduction: Neural network free online book, has written three chapters, there are corresponding open source code: github.com/mnielsen/ne… [82] Good news for hobbyists.

Java Machine Learning^[83]

Introduction: Java machine Learning platform and open source machine Learning library, according to the classification of big data, NLP, computer vision and Deep Learning. Looks quite complete, Java enthusiasts worth collecting.

Machine Learning Theory: An Introductory Primer^[84]

Introduction: Machine learning is the most basic introduction to the article, suitable for zero foundation

Summary of Common Machine Learning Algorithms^[85]

Introduction: There are many algorithms for machine learning. A lot of the confusion people have is, a lot of algorithms are kind of algorithms, and some algorithms are extensions of other algorithms. Here, we introduce to you from two aspects, the first aspect is the way of learning, the second aspect is the similarity of algorithm.

Machine Learning Classic Papers/Survey Collection^[86]

Introduction: Look at the title you already know what is the content, yes. There are many classic machine learning papers worth reading carefully and repeatedly.

Machine Learning Video Library^[87]

Introduction: This video is produced by Caltech. English background is required.

Machine Learning Classics^[88]

Introduction: A summary of classic books on machine learning, including basic mathematics and algorithm theory books, can be used as a reference list for beginners.

16 Free eBooks On Machine Learning^[89]

16 machine learning ebooks that you can download and read on your pad or phone at any time. No. I suggest you read one and then download another.

A Large Set of Machine Learning Resources for Beginners to Mavens^[90]

Introduction: The title is big, from novice to expert. But after reading all the information on it. Must be an expert

Best Introduction to Machine Learning^[91]

Introduction: the introduction of the book is really a lot of, and I have helped you find all.

The Sibyl^[92]

Sibyl is a supervised machine learning system designed to solve predictive problems, such as YouTube video recommendations.

Neural Network & Text Mining^[93]

NLP and Text Mining for Neural Networks

Foreground Target Detection 1 (Summary)^[94]

Introduction to Computer Vision: Foreground Object Detection 1 (Summary)

Pedestrian Detection^[95]

Introduction to Computer Vision: Pedestrian detection

Deep Learning — Important Resources for Learning and Understanding^[96]

Important resources for learning and understanding. Is awesome

Machine Learning Theory: An Introductory Primer^[97]

Introduction: This is another introduction to machine learning for beginners. Worth reading

Neural Networks and Deep Learning^[98]

Neural Networks and Deep Learning ebook

Python Web Crawler & Text Processing & Scientific Computing & Machine Learning & Data Mining Weapon Spectrum^[99]

17 Tools for Machine learning in Python

The Magic Gamma Function, Part 1^[100]

Introduction: Next video here the Magic Gamma Function (part ii)[101]

The Story of Distributed Machine Learning^[102]

Introduction: The author Wang Yi is currently the director of Advertising algorithm in Tencent. After graduation, Wang Yi worked as a researcher at Google. This article is about What Dr. Wang Yi has seen and heard about distributed machine learning from Google to Tencent in the past 7 years. Worth to read

“Level-up Your Machine Learning”^[103]

Introduction: The machine learning level is divided into 0~4 levels, each level needs to learn the textbook and knowledge. In this way, machine learners are provided with a roadmap for progress, so as to avoid detours. In addition, the whole site is about machine learning, and it is very rich in resources.

Machine Learning Surveys^[104]

Introduction: an overview of all aspects of machine learning

Deep Learning Reading List^[105]

Deep learning reading resource list

Deep Learning: Methods and Applications^[106]

Introduction: This is an ebook about the methods and applications of deep learning written by Li Peng and Dong Yu, researchers of Micro

Machine Learning Summer School 2014^[107]

Introduction: CMU has just completed its machine learning summer course in July 2014 with nearly 50 hours of videos and more than a dozen PDF slides covering hot topics such as deep learning, Bayesian, distributed machine learning, scalability and so on. All 13 lecturers are brilliant: including Tom Mitchell, whose machine learning is a staple of elite schools, and CMU Li Mu.

Sibyl: Large-scale Machine Learning Systems from Google^[108]

At this year’s IEEE/IFIP International Conference on Reliable Systems and Networks (DSN), Tushar Chandra, a Google software engineer, gave a keynote address on Sibyl systems. Sibyl is a supervised machine learning system designed to solve predictive problems, such as YouTube video recommendations. Read Google Sibyl [109] for details.

Building a Deeper Understanding of Images^[110]

Introduction: Christian Szegedy of Google Research wrote on the Google Research blog briefly about the GoogLeNet system they participated in this year with ImageNet and achieved good results. It’s about image processing.

Bayesian Network and Python Probability Programming Practical Introduction^[111]

Introduction: Bayesian learning. If not, look at probabilistic programming languages and Bayesian methods in practice [112]

AMA: Michael I Jordan^[113]

“If you had one billion dollars, what would you do with it? Jordan: “I would use that $1 billion to build a NASA-level research program for natural language processing.”

Machine Learning & Data Mining Notes (16)^[114]

In addition, the author also has some other machine learning and data mining article [115] and deep learning article [116], not only the theory and source code.

Text and Data Mining Video Summary^[117]

Introduction: Videolectures’ 25 most popular text and data mining videos

How to Choose a Deep Learning GPUs^[118]

Introduction: Tim Dettmers, who regularly gets good grades on Kaggle, describes how he chose deep learning GPUs and how he personally built deep learning GPU clusters: t.cn/RhpuD1G[119]

Michael Jordan: The Deep Model^[120]

Introduction: Michael Jordan

Deep Learning and Knowledge Graph Ignite Big Data Revolution^[121]

Introduction: There are two and three parts. Blog.sina.com.cn/s/blog_46d0… [122]

Deep Learning Tutorial Translation^[123]

Introduction: It is the Deep Learning course of Stanford professor Andrew Ng, and Chinese machine Learning enthusiasts are very enthusiastic to translate this course into Chinese. If your English is not good, check this out

Deep Learning 101^[124]

Introduction: Deep learning has been hyped in the media for the past two years (just like big data). In fact, many people do not know what deep learning is. The article progresses from simple to profound. Tell you what a deep pedant is!

The UFLDL Tutorial”^[125]

Introduction: This is a free course (barely) offered by Stanford University that will give you an idea of how to learn in depth. There are some basic algorithms in there. And it tells you how to apply it to the real world. The Chinese version of [126]

Toronto Deep Learning Demos^[127]

This is a deep learning demo by the University of Toronto to recognize image tags/image to text. This is a practical application case. Have a source

Deep Learning from the Bottom Up^[128]

Introduction: Machine learning models. Reading this content requires some basic knowledge.

Classification summary of R Toolkit^[129]

Introduction: (CRAN Task Views, 34 common tasks, each Task and its own classification lists several common related tools) For example: Machine learning, Natural Language Processing, Time Series analysis, Spatial information analysis, multivariate analysis, Econometrics, Psychostatistics, Sociological statistics, Stoichiometry, Environmental Science, pharmacokinetics, etc

Summary of Common Machine Learning Algorithms^[130]

Introduction: Machine learning is undoubtedly a hot topic in the field of data analysis. Many of us use machine learning algorithms at some point in our daily work. This article summarizes common machine learning algorithms for your reference in your work and study.

Deep Learning Learning Notes Collection Series^[131]

Introduction: a lot of dry goods, and the author also summarized several series. In addition, the author also provides a navigation of the article [132]. Thanks very much for the author’s summary.

Deep Learning Learning Notes Collection series (2)^[133]

Deep Learning (Deep Learning)^[134]

Deep Learning Learning Notes Collection series (4)^[135]

Deep Learning Learning Notes Collection series (5)^[136]

Deep Learning Learning Notes Collection series (6)^[137]

Deep Learning Learning Notes Collection series (7)^[138]

DeepLearning notes organizing series (8)^[139]

Tutorials on Session A-deep Learning for Computer Vision^[140]

Introduction: Delivery Reason: NIPS 2013 tutorial on Making Computers with Deep Learning by Rob Fergus. [141] He is a professor at New York University and currently works at Facebook. His 8 papers from 2014 [142]

“FudanNLP”^[143]

FudanNLP is an open source Chinese natural language processing (NLP) toolkit developed by The School of Computer Science of Fudan University. FudanNLP contains Chinese word segmentation, keyword extraction, named entity recognition, part of speech tagging, time word extraction, grammar analysis and other functions, which are extremely valuable for search engine text analysis.

Open Sourcing ML-ease^[144]

LinkedIn is an open source machine learning toolkit that supports stand-alone, Hadoop Cluster, and Spark Cluster with an emphasis on Logistic Regression algorithms

Journal of Machine Learning^[145]

Introduction: For English is not good, but also want to learn machine learning friends. It’s a big bonus. At present, machine Learning weekly mainly provides Chinese version, still for the majority of domestic enthusiasts, covering machine learning, data mining, parallel systems, image recognition, artificial intelligence, robotics and so on. Thank you for the author

Linear Algebra^[146]

Introduction: Linear Algebra is an important mathematical precursor course for Machine Learning. In fact, “line generation” this course is easy to understand, especially not easy, if the first to talk about inverse number and listing the nature of determinants, it is easy to let students lose interest in learning. My personal recommendation for the best linear Algebra course is by Gilbert Strang at MIT. Course Home page [147]

The Big data –^[148]

Introduction: Big data data processing resources, incomplete list of tools, from framework, distributed programming, distributed file system, key-value data model, graph data model, data visualization, column storage, machine learning, etc. Great resource summary.

Machine Learning for Smart Dummies^[149]

Yahoo invited a visiting scholar from Ben Gurion University to produce a series of video courses on machine learning. This course is divided into 7 phases, which explains in detail the basic theoretical knowledge of SVM, Boosting, Nearest Neighbors, decision trees and other conventional machine learning algorithms.

Entanglement-Based Quantum Machine Learning^[150]

The First Experiment of Quantum Machine Learning in the Era of Big Data [151]

How a Math Genius Hacked OkCupid to Find True Love^[152]

Wired magazine reported that UCLA math PhD Chris McKinlay (Figure 1) used big data and machine learning to crack the matchmaking algorithm of a dating website and find love. He downloaded 6 million answers to questions from 20,000 female users of a dating website by controlling 12 accounts using Python scripts. After statistical sampling and cluster analysis (FIG. 2,3), they finally harvested true love. Science and technology change destiny!

The Underactuated Robotics,”^[153]

Introduction: Underactuated Robotics of MIT will start on October 1, 2014. This course is a graduate level course of MIT. If you are interested in Robotics and nonlinear dynamical systems, you may wish to challenge this course!

Mllib Practice (1)^[154]

Introduction: Sharing of mlLIB practice experience

Google Turns To Deep Learning Classification To Fight Web Spam^[155]

Google antispam with Deep Learning

NLP Common Information Resources^[156]

Introduction :NLP Common Information Resources * NLP Common Information Resources [157]

Machine Learning Checklist^[158]

Introduction: Machine learning checklist

Best Papers vs. Top Cited Papers in Computer Science^[159]

Introduction: Most cited paper in computer science since 1996

InfiniTAM: Depth Image-based Volume Data Integration Framework^[160]

Introduction: this year a ACM Trans. On Graphics (TOG) paper code collation into an open source algorithm framework, shared out. Welcome to use it. It can collect 3D data and reconstruct 3D model in real time. Online learning, GPU Random Forest and GPU CRF will also be released later.

Hacker’s Guide to Neural Networks^[161]

Now, the most popular is Deep Learning, how to learn it better? Karpathy, author of ConvNetJS[162], a cool open source project that lets you run deep learning in your browser, says the best thing is that it becomes clear when you start writing code. He just released a book, updated online

Building a Production Machine Learning Infrastructure^[163]

Introduction: Former Google advertising systems engineer Josh Wills talks about the similarities and differences between machine learning in industry and academia

Deep Learning Sentiment Analysis for Movie Reviews using Neo4j^[164]

Introduction: Use Neo4j[165] for sentiment analysis of film reviews.

DeepLearning.University — An Annotated DeepLearning Bibliography^[166]

Introduction: not only the materials, but also some of the materials are annotated.

A Primer on Deeping Learning^[167]

Introduction: Deep learning primer

Machine Learning is Teaching us the secret to Teaching^[168]

Introduction: What can Machine Learning teach us?

Scikit-learn: Python Modules for Machine Learning^[169]

Scikit-learn is a Python module for machine learning built on SciPy.

Michael Jordan: Deconstructing models in the Field^[170]

Introduction: Professor Michael I. Jordan is a master of neural networks in the field of machine learning. He has a strong interest in deep learning and neural networks. As a result, many of the questions that were asked included various models of machine learning, which Dr. Jordan explained and envisioned.

Short Visual Tutorial for A* Search Algorithm^[171]

Introduction: A* search is the basic algorithm of artificial intelligence, used to efficiently search the best path of two points in the graph, the core is G (n)+ H (n): G (n) is the actual cost from the starting point to vertex N, h(n) is the estimated cost from vertex N to the target vertex. Collection of [172]

FudanNLP, An Open Source Cloud-based Natural Language Processing Project^[173]

Introduction: This project uses Microsoft Azure, which can complete the deployment of NLP on Azure Website in a few minutes, immediately start the trial of various features of FNLP, or call the language analysis function of FNLP in the form of REST API

Probability Thematic Model & Data Science Fundamentals^[174]

Introduction: Currently, he is the chief professor and doctoral supervisor of computer software in Fudan University. Deputy Director of computer Science Research Institute. Internal course

Introduction to Machine Learning Resources Incomplete Summary^[175]

Introduction: good things of dry goods really a lot

Collection of Literature on deep Learning since 2014^[176]

Introduction: from hardware, image, health, biology, big data, biological information and quantum computing, Amund Tveit et al. maintained a small deeplearning. University project: collecting DeepLearning literature since 2014, which we believe can be used as a starting point for DeepLearning. Github [177]

Two Applied Papers on Stock Trend in EMNLP^[178]

Introduction: Two articles on STOCK Trend [179] in EMNLP used deep model for organizational characteristics; Exploiting Social Relations and Sentiment for Stock Prediction[180] used Stock network.

Bengio Group (LISA Group, University of Montreal) Deep Learning Course^[181]

Introduction: the author is a deep learning leader Bengio group to write a tutorial, algorithm in-depth show, there are implementation code, step by step.

Neural Turing Machine for Learning Algorithms^[182]

Introduction: A lot of traditional machine learning tasks are learning functions, but Google has a tendency to start learning algorithms. Google’s Learning to Execute[183] also has similarities

Learning to Rank for Information Retrieval and Natural Language Processing^[184]

Introduction: The author is huawei Technologies Co., LTD., Noah’s Ark laboratory, chief scientist Dr. Li Hang wrote about information retrieval and natural language processing article

Rumor has It: Identifying Misinformation in Microblogs^[185]

Introduction: Using machine learning in the discrimination of rumors application, in addition to two. One is paper that identifies garbage and false information [186]. The other is online public opinion and its analysis technology [187]

R Machine Learning Practice^[188]

Introduction: This course is a paid course of netease open course, not expensive, super cheap. It is mainly suitable for those who are interested in machine learning and data mining using R language.

Big Data Analytics: The Evolution of Machine Learning Algorithm Implementations^[189]

Introduction: In this chapter, the author summarizes the evolution of the implementation of three generations of machine learning algorithms: the first generation is non-distributed, the second generation tools such as Mahout and Rapidminer implement hadoop-based extensions, and the third generation tools such as Spark and Storm implement real-time and iterative data processing. BIG DATA ANALYTICS BEYOND HADOOP[190]

Image Processing, Analysis and Machine Vision^[191]

Introduction: One of the four fantastic books on computer vision, the other three books are Hartley’s Polygraph Geometry, Gonzalez’s Digital Image Processing, Rafael C.Gonzalez/Richard E. Oods’ Digital Image Processing [192]

LinkedIn’s Latest recommendation System article Browsemaps^[193]

Introduction: There is little in the way of algorithms, but the authors introduce the many applications of CF on LinkedIn and some of their experiences in making recommendations. The final lesson is that you should monitor the quality of log data, because the quality of recommendations depends on the quality of the data!

How to Consult Academic Materials in natural Language Processing (NLP) for Beginners^[194]

Introduction: How do beginners access academic materials in the field of natural Language processing (NLP)

Raspberry PI facial Recognition Tutorial^[195]

Introduction: Face recognition with raspberry PI and camera module

Building conversational Systems using Deep Learning and Big Data^[196]

Introduction: How to use deep learning and Big Data to build dialogue system

Leo Breiman: Statistical Modeling: The Two Cultures^[197]

Introduction: A new Review of Sparse Modeling by Francis Bach: Sparse Modeling for Image and Vision Processing, covering Sparsity, Dictionary Learning, PCA, Matrix Factorization and other theories. And the first part of Why does the L1-norm induce sparsity is pretty good.

Reproducing Kernel Hilbert Space^[198]

Introduction: RKHS is an important concept in machine learning, and its application in large margin classifier is also widely known. Without a good mathematical foundation, it may be difficult to understand RKHS directly. This article goes from basic Spaces to Banach and Hilbert Spaces in 12 pages.

Hacker’s Guide to Neural Networks^[199]

Introduction: Many students are confused about machine learning and deep learning because they have a general understanding of mathematics, but do not know how to write codes by hand. Stanford Deep Learning PhD Andrej Karpathy has written a hands-on deep learning and machine learning tutorial to teach you how to write neural networks and SVM in Javascript.

Summary of Corpus Resources^[200]

【 Corpus 】 Corpus resource summary

Journey of Machine Learning Algorithms^[201]

Introduction: This article will go through the most popular machine learning algorithms, and it will be helpful to get a general idea of which methods are available.

Reproducible Research in Computational Science^[202]

Introduction: There are a lot of source code (or executable code) and related papers about machine learning, signal processing, computer vision, deep learning, neural networks and other fields. Good resources for scientific research writing papers

NYU Deep Learning Course Materials in 2014^[203]

Introduction: NYU 2014 Deep Learning course materials, with videos

Incomplete Summary of Computer Vision Data Sets^[204]

Introduction: Incomplete summary of computer vision data sets

Machine Learning Open Source Software^[205]

Machine learning Open source software

“LIBSVM”^[206]

A Library for Support Vector Machines

Support Vector Machines^[207]

Introduction: Data mining ten classical algorithms [208]

100 Best GitHub: Deep Learning^[209]

Github offers 100 awesome projects

University of California, Irvine machine Learning Dataset Repository^[210]

Uc Irvine currently maintains 306 data sets for the machine learning community. Querying data sets [211]

Andrej Karpathy personal Homepage^[212]

Andrej Karpathy is a PhD student of Li Fei-Fei from Stanford University. He has made breakthroughs in research and engineering in the field of semantic analysis of image and video using machine learning.

Andrej Karpathy demonstration of Deep Reinforcement Learning^[213]

Introduction: Andrej Karpathy’s Demonstration of Deep Reinforcement learning, paper here [214]

CIKM Data Mining Competition Winning Algorithm – Chen Yunwen^[215]

Introduction: CIKM Cup(or CIKM Competition) is the name of an international data mining Competition organized by ACM CIKM.

Geoffrey E. Hinton^[216]

Deep Learning theory and Practice in Natural Language Processing^[217]

Introduction: Powerpoint presentation of deep Learning Theory and Practice of Natural Language Processing in CIKM2014 by Deep Learning Technology Center of Microsoft Research

Stock Price Prediction using Big Data and Machine Learning^[218]

In this paper, Apache Spark and Spark MLLib are used to construct a price movement prediction model from order log data of The New York Stock Exchange based on Dynamic Modeling of High-frequency Limit Orders using Support Vector Machines. GitHub source code hosting address [219].

Some Theoretical Issues on Machine Learning^[220]

Introduction: Academician Xu Zongben will discuss several theoretical problems about machine learning with friends who love machine learning, and give some meaningful conclusions. Finally, some examples are given to illustrate the physical significance and practical application value of these theoretical problems.

Deep Learning in Natural Language Processing^[221]

Introduction: The author is also the author of This is The Search Engine: The Core Technology, which focuses on the application layer

“Undergraduate Machine learning at UBC”^[222]

Introduction: Machine learning courses

Face Recognition N must-read articles^[223]

Introduction: Face recognition must read article recommended

Classic Papers and Literature on Recommendation System and Its Application in the Industry^[224]

Introduction: Recommend system classical papers literature

Face Recognition N must-read articles^[225]

Introduction: Face recognition must read article recommended

The 12th China Seminar on Machine Learning and Its Applications^[226]

Introduction: PPT of the 12th China Seminar on Machine Learning and Its Applications

Statistical Machine Learning^[227]

Introduction: Statistical learning is a science about the probabilistic statistical model constructed by computer based on data and the use of the model to predict and analyze the data. Statistical learning is also known as statistical machine learning. The course is from Shanghai Jiao Tong University

Introduction to Machine Learning^[228]

The goal of machine learning is to program a computer to solve a given problem using sample data or past experience.

Slides of CIKM 2014 Keynote Report^[229]

Introduction: CIKM 2014 Slides of keynote presentations by Jeff Dean, Qi Lu, Gerhard Weikum, and Industry Track presentations by Alex Smola, Limsoon Wong, Tong Zhang, Chih-Jen Lin

Interesting Open Source Projects in ARTIFICIAL Intelligence and Machine Learning^[230]

Introduction: Part of Chinese List [231]

“Machine Learning Classical Algorithms and Python Implementation — SVM Classifier Based on SMO”^[232]

Introduction: The author also has a meta-algorithm, AdaBoostpython implementation article [233]

Numerical Optimization: Understanding L-BFGS^[234]

Introduction: Dr. Aria Haghighi of the University of California, Berkeley, has written an excellent blog post on numerical optimization, from Newton’s method to quasi-Newton’s method, to BFGS and L-BFGS, with illustrations and pseudocode. Highly recommended.

Brief Overview of Deep Learning Methods (1)^[235]

Introduction: There is a sequel to a concise Overview of Deep Learning Methods (II) [236]

R Language for Programmers^[237]

R language programmer private custom version

Deciphering Google Maps: Big Data and Machine Learning^[238]

Introduction: Google Maps decryption

Common Methods of Spatial Data Mining^[239]

Introduction: Common methods of spatial data mining

Use Google’s Word2Vec for Movie Reviews^[240]

Kaggle: When Bag of Words Meets Bags of popcorn aka: NLP with Word2VEc and Deep Learning The full tutorial teaches step by step the word2vec model using Python and gensim packages, as well as tuning parameters and cleaning data in real games. Don’t forget to upgrade if you have installed GenSim

“PyNLPIR”^[241]

PyNLPIR provides a Python interface for NLPIR/ICTCLAS Chinese word segmentation. In addition, Zhon[242] provides common Chinese character constants such as CJK characters and radicals, Chinese punctuation, pinyin, and Chinese regular expressions (such as finding traditional characters in text).

Deep Convolutional Neural Network playing Go^[243]

Introduction: this article says to apply the recent model recognition breakthrough to go software, playing 160,000 professional chess spectrum training model recognition function. That’s a good idea. After training, I can give the next step, about 10 levels of chess power, without calculating, just looking at the board. But this article is too optimistic, saying that the last bastion of humanity is about to fall. Too soon. However, if combined with other software should have potential to dig. @ million oil dark green

NIPS Manuscript Review Experiment^[244]

Introduction :UT Austin professor Eric Price detailed analysis of this year’s NIPS review experiment, he said that based on the results of this experiment, if NIPS review again this year, half of the papers would be rejected.

The Best Big Data, Data Science Articles of 2014^[245]

KDNuggets has rounded up the 14 most read and shared articles of 2014. There are a number of themes — deep learning, careers as data scientists, education and compensation, tools for learning data science like R and Python, and popular voting for the most popular data science and data mining languages

Linear Regression Algorithms for Machine Learning^[246]

Introduction: Linear regression is implemented in Python, and the author has other great articles to recommend

2014 China Big Data Technology Conference speech by 33 core experts PDF^[247]

Introduction: 2014 China Big Data Technology Conference 33 core experts speech PDF download

Emotional Analysis using RNN and Paragraph Vector^[248]

Introduction: Ensemble of Generative and Discriminative Techniques for Sentiment Analysis of Movie Reviews by T. Mikolov & Y. Bengio , using RNN and PV in sentiment analysis effect is good, [project code] (github.com/mesnilgr/ic… [249] Vector has finally been unveiled.

Technical Presentation at NLPIR/ICTCLAS2015 Word Segmentation Conference^[250]

NLPIR/ICTCLAS2015 word segmentation system release and user communication conference speech, please more friends review the new word segmentation. Our lab students’ lectures include: Sun Mengshu – Research on Product Search Technology based on Comment Mining [251] Li Ran – Topic Model [252]

Machine Learning is Fun!^[253]

Introduce Convex Neural Networks to resolve the dimension disaster

CNN’s Reverse Derivation and Exercises^[254]

Introduction: How to train CNN parameters when using BP algorithm is introduced. After all, CNN has convolution layer and lower sampling layer. Although it is essentially the same as THE BP algorithm of MLP, there are some differences in form. In addition, the author also makes a resource set: machine learning, deep learning, vision, mathematics, etc. [255]

Regular Expression Optimization into a Trie Tree^[256]

Introduction: What if you need to match 100,000 keywords in an article? The Aho-Corasick[257] algorithm can complete the matching in linear time by using the Trie tree with added return edges. But what if you match 100,000 regular expressions? The method of optimizing multiple regulars into Trie trees can be used, such as Regexp::Trie by the Japanese [258]

Deep Learning Reading List^[259]

Introduction: Deep learning reading lists

The Caffe^[260]

Caffe is an open source deep learning framework. The author is currently working at Google. The author is Yangqing Jia.

“Caffe Repetition of the GoogLeNet Deep Learning Model”^[262]

Introduction :2014 ImageNet champion GoogLeNet Deep Learning Model Caffe replica model, GoogLeNet Paper [263].

LambdaNet, Haskell Implementation of open Source Artificial Neural Network Library^[264]

LambdaNetLambdaNet is an open source artificial neural network library implemented by Haskell. It abstracts network creation, training, and use of higher-order functions. The library also provides a set of predefined functions that users can combine in a variety of ways to manipulate real-world data.

Baidu Yu Kai & Zhang Tong Machine Learning Video^[265]

Introduction: If you work in Internet search, online advertising, user behavior analysis, image recognition, natural language understanding, or bioinformatics, intelligent robotics, or financial forecasting, then this core course must be thoroughly understood.

Yang Qiang talks about the Origin of Intelligence at TEDxNanjing^[266]

Introduction :” There are many schools of artificial intelligence research. IBM, for one, believes that all you need is high performance computing to get intelligence, and their Deep Blue beat the world chess champion. Another school holds that intelligence comes from animal instinct; There’s a strong school of thought that says you just take experts, write them down logically, put them on a computer…” Yang Qiang talks at TEDxNanjing about the origins of intelligence

“Deep RNN/LSTM for Structured learning 0) sequence annotation Connectionist Temporal ClassificationML06”^[267]

Introduction :1) Machine Translation Sequence to Sequence NIPS14[268] 2) Composition AS FOREIGN LANGUAGE[269]

Word2vec for Deep Learning^[270]

Introduction: Analysis documents of Word2VEc written by three engineers of netease Youdao, from basic word vector/statistical language model ->NNLM-> log-Linear/log-bilinear -> hierarchical log-bilinear, to CBOW and Skip-Gram models, There are tricks, formulas and codes for Word2vec, which is basically a collection of information about Word2vec on the Web. If you are interested in word2vec, check it out

Machine Learning Open Source Software^[271]

Machine learning open Source software, includes a variety of machine learning programming languages academic and commercial open source software. Dmoz-computers: Artificial Intelligence: Machine Learning: Software[272],LIBSVM — A Library for Support Vector Machines[273],Weka 3: Data Mining Software in Java[274], SciKit-Learn :Machine Learning in Python[275],Natural Language NLTK[276], Daily Tasks and Daily Tasks [278], Daily Tasks and Daily Tasks Vision Library[279]

Machine Learning Beginner’s Guide^[280]

Introduction: The author is a second year graduate of Computer Science, majoring in natural language processing. This is a point he speaks from experience. For the introduction of friends may be helpful

A Tour of Machine Learning Algorithms^[281]

This is an article about machine learning algorithm classification, very good

Machine Learning Journal 2014 Collection^[282]

Machine Learning Daily has a lot of content to recommend, and some of the best content here is from machine Learning Daily.

Common models of Image Classification with Deep Learning^[283]

Introduction: This is an article about image classification in deep learning

Automatic Speech Recognition: A Deep Learning Approach^[284]

Introduction: The author, with Bengio’s brother Samy, co-edited Automatic Speech Recognition: A Nuclear Approach (2009) 3) Kai-fu Lee’s 1989 monograph on Automatic Speech Recognition, with a foreword by Raj Reddy, PhD director and 1994 Turing Prize winner

Chinese Word Segmentation techniques in NLP^[285]

Introduction: The author is a member of 360 e-commerce technology group. This is an application of NLP in Chinese word segmentation

Using convolutional Neural Nets to Detect Facial Keypoints Tutorial^[286]

Introduction: Face keypoint detection using Deep Learning, in addition to an AWS deployment tutorial [287]

Book Recommendation :Advanced Structured Prediction^[288]

Introduction: The new book Advanced Structured Prediction (t.cn/RZxipKG[289], compiled by Sebastian Nowozin et al., published by MIT, is a collection of literature in the field of Structured Prediction, including CV and NLP. It is worth reading. Several draft chapters available online: I [290], II [291], III [292], IV [293], V [294]

An Introduction to Matrix Concentration Inequalities^[295]

Introduction: Tropp has written matrix probability inequalities written by mathematicians in advanced and advanced mathematical languages with elementary methods. It is a very good manual, and all kinds of proofs in the field are based on the results in Tropp. It’s elementary, but it’s very difficult

The Free Big Data Sources You Should Know^[296]

Introduction: You can’t miss the free big data sets, some of which are familiar to you, some of which may be new to you. The content spans text, data, multimedia, etc. Let them accompany you on your journey of data science, including: Data.gov, US Census Bureau, European Union Open Data Portal, data.gov.uk, etc

A Brief Overview of Deep Learning^[297]

Introduction: Deep learning review and practical suggestions by Ilya Sutskever, a Google scientist and disciple of Hinton

A Deep Dive into Recurrent Neural Nets^[298]

Introduction: very good discussion of recursive neural network article, covering the concept of RNN, principle, training and optimization and other aspects of the content, strongly recommended! Nikhil Buduma has another good book, Deep Learning in a Nutshell[299]

Machine Learning: Learning Resources^[300]

Introduction: It integrates many resources, such as contests, online courses, demos, data integration and so on. Have the classification

Statistical Foundations of Machine Learning^[301]

Introduction: “Statistical Fundamentals of Machine Learning” online edition, this manual hopes to find a balance between theory and practice, each main content is accompanied by practical examples and data, examples in the book are written in R language.

A Deep Learning Tutorial: From Perceptrons to Deep Networks^[302]

Deep Learning Guidance: From Shallow Perceptrons to Deep Networks by IVAN VASILEV. High readable

Research Priorities for Robust and Beneficial Artificial Intelligence^[303]

Introduction: Robust and Beneficial AI Research Priorities: An open letter, so far Stuart Russell, Tom Dietterich, Eric Horvitz, Yann LeCun, Peter Norvig, Tom Mitchell, Geoffrey Hinton, Elon Musk et al signed The Future of Life Institute (FLI)[304]. The letter comes against the backdrop of recent warnings by Stephen Hawking and Elon Musk about the potential threat of AI. In the open letter, AI scientists looked into the future development direction of ARTIFICIAL intelligence from the perspective of benefiting society, and proposed four requirements of AI system development: Verification, Validity, Security and Control, as well as social problems that need attention. After all, AI is currently poorly studied in economic, legal, and ethical fields. In fact, there is another American TV series Person of Interest [305], which introduces the evolution of AI from self-learning, filtering, image recognition and voice recognition to danger judgment at the beginning, to the state that machines want to control the world after learning and growing up in the fourth season. Speaking of which, I recommend watching.

“Metacademy”^[306]

Introduction: It provides many resources according to the entry, as well as related knowledge structure, road map, length of time, etc. It claims to be a “machine learning” search engine

FAIR Open Sources Deep – Learning Modules for Torch^[307]

Introduction: The Facebook Ai Institute (FAIR) has opened source a series of software libraries to help developers build bigger and faster deep learning models. Open software libraries are called modules at Facebook. Using them instead of the default modules in Torch, a development environment commonly used in machine learning, allows training of larger scale neural network models in less time.

Analysis of Haar Classifier Method for Face Detection^[308]

Introduction: Although this article was written in 2012, it is entirely based on the author’s experience.

How to Be a Data Scientist^[309]

This is an interview with Peter Harrington, author of Machine Learning in action. Contains answers to some of the questions in the book and a bit of personal study advice

Deep Learning from the Bottom Up^[310]

Introduction: A very good overview of deep learning. Several popular deep learning models are introduced and discussed

Hands-on Data Science with R Text Mining^[311]

Introduction: mainly describes the use of R language data mining

“Understanding Convolutions”^[312]

Conv Nets are Modular Perspective[313], Groups & Group Convolutions[314]. Conv Nets are A Modular Perspective[313], Groups & Group Convolutions[314]. The author’s other articles on neural networks are also excellent

Introduction to Deep Learning Algorithms

Introduction: The introduction of Deep Learning algorithm, which introduces the rise of Deep Learning in 2006 three papers

Learning Deep Architectures for AI^[315]

Introduction: A book for learning artificial Intelligence by Yoshua Bengio, related domestic reports [316]

Geoffrey E. Hinton’s Personal Page^[317]

Geoffrey Hinton is the master of Deep Learning. His homepage contains some introductory articles and courseware worth studying

PROBABILITY THEORY: THE LOGIC OF SCIENCE^[318]

Introduction: Probability theory: A book on mathematical logic

“H2O”^[319]

A mathematical library for fast statistics, machine learning, and large data volumes

ICLR 2015 arXiv Collection^[320]

Introduction: Here you can see what’s new in deep learning recently.

Introduction to Information Retrieval^[321]

Is introduced: the book a household name in the field of information retrieval, in addition to providing the free version, also provide a list of the IR resources [322], and includes information retrieval, network information retrieval, search engine implementation related books, research center and related courses, sub-fields, conferences, journals, etc., is a complete, it is worth collecting

Information Geometry and Its Applications to Machine Learning^[323]

Information geometry and its application to machine learning

Legal Analytics — Introduction to the Course^[324]

Introduction: Powerpoint presentation of legal Analysis. Machine learning is used to solve the problem of legal correlation analysis and prediction. The relevant legal applications include predictive coding, early case evaluation, case overall prediction, pricing and staff prediction, judicial behavior prediction, etc. Everyone in legal field may be stranger, might as well understand.

Algorithms on Text^[325]

Introduction: Optimization, model, maximum entropy and so on are mentioned in this paper. The recommendation system is a good read. It also recommends Generative Model and Discriminative Model[326]

“NeuralTalk”^[327]

Introduction: NeuralTalk is a Python+numpy project for learning Multimodal Recurrent Neural Networks that describe images with Sentences.NeuralTalk is a Python tool for generating natural language descriptions from images. It implements the algorithms of Google (Vinyals et al., CNN + LSTM) and Stanford (Karpathy and Fee-Fei, CNN + recursive neural network RNN). NeuralTalk comes with a trained animal model, which you can try with pictures of lions and elephants

Deep Learning on Hadoop 2.0^[328]

Introduction: this article introduces the use of deep learning on Hadoop2.0, from paypal

Practical Recommendations for gradient-based Training of Deep architectures^[329]

Introduction: Practical recommendation guidance for training depth frameworks with a Gradient descent based approach by Yoshua Bengio[330]. Thanks to @xuewei4d

Machine Learning With Statistical And Causal Methods^[331]

Introduction: Machine Learning with Statistical and Causal Approaches (Video Presentation)

Machine Learning Course 180 ‘^[332]

Introduction: A Youtube video on machine learning. 160 sets. The system is comparable to a book.

Regression, Gradient Descent^[333]

The author’s research interests are machine learning and parallel computing. If you want to know more, check out his blog [334]

Meituan Recommendation Algorithm Practice^[335]

Introduction: Meituan recommendation algorithm practice, from the framework, application, strategy, query analysis

Deep Learning for Answer Sentence Selection^[336]

Introduction: Deep learning is used to select answer sentences in question answering system

Learning Semantic Representations Using Convolutional Neural Networks for Web Search^[337]

CNN for WEB search, deep learning in text computing applications

Awesome Public Datasets^[338]

Introduction: Public data sets in the Awesome series

Search Engine & Community^[339]

An academic search engine

“SpaCy”^[340]

Cython is an industrial-grade natural language processing library written in Python and Cython. It is claimed to be the fastest NLP library, fast because one is written in Cython, and the other is to use a very clever hash technology to speed up the system bottleneck, the access of sparse features in NLP

Collaborative Filtering with Spark^[341]

Introduction: Fields[342] is a mathematics research center. The slides above are from Russ Salakhutdinov’s presentation on Large-scale Machine Learning at an event held by Fields

Topic Modeling Classic Papers^[343]

Introduction: Topic Modeling is a classic paper that highlights key points

Move Evaluation in Go Using Deep Convolutional Neural Networks^[344]

Introduction: A new paper from the University of Toronto, in collaboration with Google, shows that deep learning can also be used to play go, supposedly at a six-dan level

Journal of Machine Learning no. 2^[345]

Introduction: News, paper, courses, Book, System,CES,Roboot, in addition to a recommended introduction and overview of deep learning materials [346]

Learning More Like a Human: 18 Free eBooks on Machine Learning^[347]

18 Free eBooks on Machine Learning

Recommend :Hang Li Home^[348]

Chief scientist of Noah’s Ark Lab of Huawei Technologies.He worked at the Research Laboratories of NEC Corporation during 1990 and 2001 and Microsoft Research Asia during 2001 and 2012.Paper[349]

DEEPLEARNING.UNIVERSITY — AN ANNOTATED DEEPLEARNING BIBLIOGRAPHY^[350]

Deeplearning. UNIVERSITY has collected 963 classified papers on DEEPLEARNING, including many classic papers

Mlmu.cz-radim Řeh sounds ek-Word2vec & Friends (7.1.2015)^[351]

Presented by Radim Řeh sounds ek(Gensim developer) at a machine learning conference. It’s very practical about word2vec and its optimizations, applications and extensions. Domestic Network Disk [352]

Introducing Streaming K-Means in Spark 1.2^[353]

Introduction: Many companies are using machine learning to solve problems and improve the user experience. So how can machine learning be made more real-time and effective? Streaming K-means in Spark MLlib 1.2, written by Brain neuroscientist Jeremy Freeman of Zebrafish Neuroresearch, was originally intended to process in real time a terabyte of their research data every half hour, and is now available for everyone to use.

Introduction to LDA and Java Implementation^[354]

Introduction: This is an introduction to LDA for engineers and provides a Java implementation out of the box. This paper only records the basic concepts and principles, and does not involve formula derivation. The core part of the LDA implementation in this paper adopts ldagibsSampler of Arbylon and has been annotated to the best of its ability. It is well tested on sogou Classification corpus and open source on GitHub[355].

Iner – Open Science Platform^[356]

Introduction: AMiner is an academic search engine that mines deep knowledge from academic networks and is oriented to scientific and technological big data mining. Collected nearly 40 million author information, 80 million paper information, more than 100 million citation relationships, links nearly 8 million knowledge points; Support expert search, institution ranking, research achievements evaluation, conference ranking.

What are some Interesting Word2Vec Results?^[357]

Introduction: This Quora topic discusses interesting uses of Word2Vec, Omer Levy mentions his analysis results and new methods in CoNLL2014 Best Paper, And Daniel Hammack gives a small application for finding unusual words and provides (Python) code [358]

Machine Learning Open Course Summary^[359]

Introduction: Machine learning open course summary, although some of the courses in it have been filed, there are some other information is not. Thanks for the course map

A First Course in Linear Algebra^[360]

Introduction: A First Course in Linear Algebra Robert Beezer has an answer. A mobile, printed version using the GNU Free Documentation Protocol quotes Jefferson’s 1813 letter

“Libfacedetection”^[361]

Libfacedetection is an open source face recognition library developed by Shenzhen University. It contains two algorithms for face detection: frontal and multi-view. Advantages: fast speed (OpenCV Haar + Adaboost 2-3 times), high accuracy (FDDB non-public class evaluation ranked second), can estimate face Angle.

Inverting a steady-state^[362]

Introduction: The best paper in WSDM2015 uses Markov chain theory in graph analysis, which is more profound than the general Propagation model. The influence coefficient model of each node is solved by global stationary distribution. The assumption is reasonable (the transfer is affected by adjacent influence coefficients). It can be used to inversely calculate the influence coefficient of each node

Introduction to Machine Learning^[363]

Introduction: Introduction to Machine learning, specific introduction [364]

The Trouble with SVMs^[365]

Introduction: a great article highlighting the importance of feature selection for classifiers. In emotion classification, the naive Bayes classifier is used to reduce the dimension of complex high-dimensional features based on mutual information, which achieves a more ideal effect than SVM, and the training and classification time is greatly reduced — more importantly, there is no need to spend a lot of time on learning and optimizing SVM — features are also no free lunch

Rise of the Machines^[366]

Introduction: Larry Wasserman, a professor of computing and computer science at CMU, contrasts the differences between statistics and machine learning in Rise of the Machine

How machine Learning solves problems^[367]

Introduction: With the advent of the era of big data, machine learning has become an important and key tool for solving problems. Machine learning is a hot field in both industry and academia. However, the academic and industrial circles have different focuses on machine learning. The academic circle focuses on the study of machine learning theory, while the industrial circle focuses on how to use machine learning to solve practical problems. This article is an actual combat in the actual environment of The United States

Gaussian Processes for Machine Learning^[368]

Introduction: Gauss Process for Machine Learning, Chapter Summary: Regression, classification, Covariance function, Model selection and hyperparameter optimization, Gauss Model and other models, Approximation methods for Large data sets, etc., microplate download [369]

FuzzyWuzzy: Fuzzy String Matching in Python^[370]

Text fuzzy matching library in Python It can calculate inter-string ratio(simple similarity coefficient), partial_ratio(local similarity coefficient), Token_sort_ratio (similarity coefficient of word ordering), token_set_ratio(similarity coefficient of word set), etc. Github [371]

“Blocks”^[372]

Introduction :Blocks is a neural network building framework based on Theano, integrating related functions, pipes and algorithms to help you create and manage NN modules faster.

Introduction to Machine Learning^[373]

Introduction: Alex Smola, the great god of Machine Learning, has recently launched a new CMU Introduction to Machine Learning course “Introduction to Machine Learning”. The 4K HD video of the course has been synchronized to Youtube. It has just been updated to 2.4 Exponential Families, playlist[374], which is a great way to get started.

Collaborative Feature Learning from Social Media^[375]

Introduction: Using social user behavior to learn the collaborative features of pictures can better express the content similarity of pictures. Because it does not rely on manual labels (labels), it can be used for large-scale image processing and is difficult to obtain and clean user behavior data. The idea of using socialized characteristics is worth learning.

Introducing Practical and Robust Anomaly Detection in a Time Series^[376]

Introduction: The technical team of Twitter introduced the open source time series anomaly detection algorithm (S-H-ESD)R package some time ago, in which the definition and analysis of exceptions are worth referring to. It is also mentioned in the paper that exceptions are strongly targeted, and the anomaly detection developed in one field can not be directly used in other fields.

Empower Your Team to Deal with Data-quality Issues^[377]

Introduction: Focusing on the response to data quality problems, which are critical to the performance and efficiency of enterprises of all sizes, the paper summarizes (but not limited to)22 typical signals of data quality problems and typical data quality solutions (cleaning, de-duplication, unification, matching, clearance, etc.)

Resources for Chinese Word Segmentation^[378]

Introduction to Chinese word segmentation resources.

Deep Learning Summit, San Francisco, 2015^[379]

Introduction: 2015 San Francisco Deep Learning Summit Video Highlights, Domestic cloud disk [380]

Introduction to Conditional Random Fields^[381]

Introduction: Excellent condition along with airport (CRF) introduction article, author’s study notes

A Fast and Accurate Dependency Parser Using Neural Networks^[382]

Stanford, a fast and accurate dependency resolver using neural networks

Which GPU(S) to Get for Deep Learning: My Experience and Advice for Using GPUs in Deep Learning^[383]

Introduction: How to choose GPU in deep learning

Sparse Linear Models^[384]

Introduction: Stanford professor Trevor Hastie’s presentation on H2O. Ai meet-up on sparse linear models — Linear models for “wide data” (feature dimensions exceeding sample numbers),13 annual presentation on the same topic [385], handout [386].

Awesome Computer Vision^[387]

Introduction: Machine vision related resources list, adhering to the Awesome series style, quality and quantity! The author also updates frequently

“Adam Szeidl”^[388]

Introduction: Social Networks Course

Building and Big-scale machine Learning Charge^[389]

Introduction: Building and deploying large-scale machine learning processes.

Face Recognition Development Kit^[390]

Introduction: Face recognition secondary development kit, free, commercial, there are demonstrations, examples, instructions.

Understanding Natural Language with Deep Neural Networks Using Torch^[391]

Torch to Understand NLP with Deep Learning Networks, article from Facebook ARTIFICIAL Intelligence.

The NLP Engine: A Universal Turing Machine for NLP^[392]

Introduction: An interesting Arxiv article by Ed Hovy of CMU and Jiwei Li of Stanford uses Shannon Entropy to describe the difficulty of various tasks in NLP.

TThe Probabilistic Relevance Framework: BM25 and Beyond^[393]

Introduction: Information retrieval sorting model BM25(Besting Matching). 1) Evolved from the classical probability model; 2) captured three factors affecting the weight of index items in the vector space model: IDF inverse document frequency; TF index item frequency; Document length normalization. 3) It also contains the idea of integrated learning: BM11 and BM15 models are combined. 4) The author is Robertson, the author of BM25 and the Okapi implementer.

Introduction to ARMA Time Series Models — Simplified^[394]

ARMA is an important method to study time series. It is composed of a mixture of autoregressive (AR) model and moving average (MA) model.

Encoding Source Language with Convolutional Neural Network for Machine Translation^[395]

Introduction: Add the attention signal from Target into the input of Source Encoding CNN, and get a better multi-neural network joint model than BBN model

Spices Form the Basis of Food Pairing in Indian Cuisine^[396]

Introduction: Uncover the secrets of Indian cuisine — through a large number of recipes of the relationship between ingredients, found that one of the reasons why Indian food is delicious is the taste of conflict, interesting text mining research

Index of HMM Related Articles^[397]

In addition, I recommend a detailed explanation of Chinese word segmentation HMM model [398]

Zipf’s and Heap’s Law^[399]

Introduction: 1) The most famous relation between word frequency and descending order is Zipf’s Law proposed by linguist Zipf (1902-1950) in 1949, that is, the two are inversely proportional. Mandelbrot (1924-2010) modified the characterization of VHF and VLF words by introducing parameters 2)Heaps’ law: vocabulary is proportional to the square root of corpus size (this is a parameter, English 0.4-0.6)

I Am Jurgen Schmidhuber, AMA^[400]

Jurgen Schmidhuber’s AMA(Ask Me Anything) on Reddit has a lot of RNN and AI, ML dry stuff about open source & ideas & methods & advice… Read patiently, I believe you will benefit a lot.

AcademicTorrents^[401]

Introduction: Into the academic data of G on T, HN recently hot topics, topics involving machine learning, NLP, SNA and so on. Download the simplest way, through BT software, RSS subscription to each collection can be

Machine Learning Interaction Checklist^[402]

Scikit-learn Cheat Sheet sciKit-Learn Cheat Sheet

A Full Hardware Guide to Deep Learning^[403]

Introduction: Comprehensive hardware guide for deep learning, from Gpus to RAM, CPU, SSD, PCIe

Pedestrian Detection Resource^[404]

Pedestrian Detection Paper & Data

A Specialized Face-Processing Network Consistent with the Representational Geometry of Monkey Face Patches^[405]

You and I are experts at face recognition, and we can recognize even subtle differences. Humans and primates have been shown to process faces differently from other species, with humans using the fusiform face area (FFA). Khaligh-razavi et al. simulated the FFA activity of face recognition by computer, which can be called the perfect combination of neuroscience and artificial intelligence.

Neural Net in C++ Tutorial^[406]

Neural network C++ tutorial, this article introduces the use of adjustable gradient descent and adjustable momentum method to design and code the classic BP neural network, network can be trained to do amazing and wonderful things out. Other posts on the author’s blog are also good.

How to Choose a Neural Network^[407]

Introduction: The NN selection reference table for practical application scenarios provided by Deeplearning4J’s official website lists the recommended neural networks for some typical problems

Deep Learning (Python, C/C++, Java, Scala, Go)^[408]

Introduction: a deep learning project, provides Python, C/C++, Java, Scala, Go multiple versions of the code

Deep Learning Tutorials^[409]

Introduction: Deep Learning Tutorial, Github [410]

Trends in Natural Language Processing: An interview with Professor Edward Howe at Carnegie Mellon University^[411]

Introduction: Trends in Natural Language Processing — an interview with Professor Edward Howe at Carnegie Mellon University.

FaceNet: A Unified Embedding for Face Recognition and Clustering^[412]

FaceNet (Labeled Faces in the Wild) : A new 99.63% accuracy rating on Labeled Faces in the Wild (LFW) FaceNet Embeddings can be used for face recognition, authentication, and clustering.

Random Forests and Boosting in MLlib^[413]

Introduction: This article is from a blog post on the Databricks website, written by Joseph Bradley and Manish Amde, This article mainly introduces Random Forests and Gradient-Vanda Trees (GBTs) algorithms and their distributed implementation in MLlib, as well as showing some simple examples and suggestions on where to start. The Chinese version of [414].

The Sum – Product Networks (SPN)”^[415]

Introduction: DNN by Pedro Domingos team at the University of Washington, providing the paper and implementation code.

Neural Network Dependency Parser^[416]

Introduction: A neural network-based natural language dependency parser (already integrated into Stanford CoreNLP) is characterized by super-fast and accurate processing of Chinese and English corpus. Based on “A Fast and Accurate Dependency Parser Using Neural Networks” [417].

Neural Network Language Models^[418]

Introduction: Based on the development history of neural network, this paper explains the form of neural network language model in each stage in detail. The models include NNLM[Bengio,2003], Hierarchical NNLM[Bengio, 2005], Log-Bilinear[Hinton, 2007],SENNA and other important deformations, which are well summarized.

Reclassifying Spam Emails Using Text and Readability Features^[419]

Introduction: New research on the Classic problem: Sorting spam using text and readability characteristics.

BCI Challenge@NER 2015^[420]

Kaggle Brain-Controlled Computer Interaction (BCI) Contest [421] is an excellent example of learning Python data processing and Kaggle’s classic contest framework

IPOL Journal · Image Processing On Line^[422]

Introduction :IPOL (Online Image Processing) is a research journal for image processing and analysis. Each article contains an algorithm and corresponding codes, demos and experimental documents. The text and source code are peer-reviewed. IPOL is an open scientific and reproducible research journal. I’ve always wanted to do something similar, to bridge the gap between product and technology.

Machine Learning Classification over Encrypted Data^[423]

Introduction: from MIT, research on efficient classification of encrypted data.

“Purine2”^[424]

Introduction: Purine: A Bi-graph based Deep Learning Framework [425], A neural network parallel framework developed by LV Laboratory in Singapore, supports the construction of various parallel architectures and basically achieves linear acceleration in the case of multiple machines and multiple cards and synchronous updating of parameters. 12 Titan can complete Googlenet training in 20 hours.

Machine Learning Resources^[426]

Introduction: This is a machine learning resource library, although relatively small. But the smallest mosquito is still meat. There is also a machine learning resource compiled by Zheng Rui [427].

Hands-on with Machine Learning^[428]

Introduction :Chase Davis’ keynote presentation material on NICAR15, using Scikit-learn as an introductory example of supervised learning.

The Natural Language Processing Dictionary^[429]

This is a natural language processing dictionary, from 1998 to now accumulated thousands of professional word explanations, if you are a beginner friend. You can borrow this dictionary to grow up faster.

PageRank Approach to Ranking National Football Teams^[430]

Introduction: By analyzing match data from 1930 to the present, PageRank is used to calculate the World Cup team leaderboard.

“R Tutorial”^[431]

An Introduction to R[432] is also recommended.

“Fast Unfolding of Communities in Large Networks”^[433]

Community Detection [434] in Gephi is based on this algorithm.

“NUML”^[435]

An open source machine learning library for.NET,github address [436]

The synaptic. Js.”^[437]

Introduction: The JS neural network library supports Node.js, can run in the client browser, supports LSTM and other github address [438]

Machine Learning for Package Users with R (1): Decision Tree^[439]

Introduction: Decision tree

Deep Learning, The Curse of Dimensionality, and Autoencoders^[440]

Deep learning autoencoders: how to deal with dimension disaster effectively, Chinese 翻译[441]

Advanced Optimization and Randomized Methods^[442]

Introduction: CMU optimization and Stochastic Methods course, by A. Smola and S. Sra, optimization theory is the cornerstone of machine learning, worth in-depth study of domestic cloud (video)[443]

Convolutional Neural Networks for Visual Recognition^[444]

Introduction: “CNN for Visual Identification” course design report collection. Nearly 100 articles, covering all aspects of image recognition applications

Topic Modeling with LDA: MLlib meets GraphX^[445]

Spark MLlib+GraphX for large-scale LDA topic extraction

Deep Learning for Multi-label Classification^[446]

Introduction: Multi-label classification based on deep learning, using DBN based on RBM to solve the multi-label classification (feature) problem

Google DeepMind Publications^[447]

Introduction: DeepMind papers collection

The Kaldi^[448]

Introduction: An open source speech recognition toolkit currently hosted on SourceForge [449]

Data Journalism Handbook^[450]

Introduction: Free e-book “Data News Handbook”, domestic enthusiastic friends translated Chinese version [451], you can also read online [452]

Data Mining Problems in Retail^[453]

Introduction: Data mining articles in retail.

Understanding Convolution in Deep Learning^[454]

Introduction: Deep learning convolutional concept details, simple.

Pandas: Powerful Python Data Analysis Toolkit^[455]

Introduction: very powerful Python data analysis toolkit.

Text Analytics 2015^[456]

Introduction: 2015 Review of text Analysis (Business) Applications.

Deep Learning Libraries and ﬁ RST Experiments with Theano^[457]

Introduction: Deep learning framework, library research and preliminary test experience report of Theano.

“DEEP learning”^[458]

About: A new book on deep learning by MIT’s Yoshua Bengio, Ian Goodfellow, Aaron Courville, and others, yet to be finalized, with online Draft Chapters to collect feedback. Amazing! Highly recommended.

“Simplebayes”^[459]

Introduction: Open source Persistent Naive Bayes library for Python.

The Paracel^[460]

Paracel is a distributed computational framework designed for machine learning problems, graph algorithms and scientific computing in C++.

HanLP:Han Language Processing^[461]

Open source Chinese language processing package.

Simple Neural Network Implementation in Ruby^[462]

Introduction: Implementing a simple neural network example using Ruby.

Hacker’s Guide to Neural Networks^[463]

Introduction to neural network hacking.

The Open Source Data Science Masters^[464]

Introduction: A lot of data scientists celebrity recommendations, as well as information.

Text Understanding from Scratch^[465]

Introduction: Implementation project has been open source on Github for Crepe[466]

Improving Distributional Similarity with Lessons Learned from Word Embeddings^[467]

Introduction: The authors found that the traditional method can achieve similar results as word2vec with tuning. Besides, no matter what the author tries, GloVe is no match for Word2vec.

CS224d: Deep Learning for Natural Language Processing^[468]

Stanford Deep Learning and Natural Language Processing lecture by Richard Socher.

Math Essentials in Machine Learning^[469]

Important Mathematical concepts in machine learning.

“Improved Semantic Representations From Tree-structured Long Short-term Memory Networks”^[470]

Introduction: Tree LSTM recursive neural network for improving semantic representation, sentence level correlation judgment and emotion classification effect is very good. Implementation code [471].

Statistical Machine Learning^[472]

Introduction: The machine learning course offered by Ryan Tibshirani and Larry Wasserman at Carnegie Mellon focuses on the application of statistical theory and methods to machine learning. The pre-requisite courses are Machine Learning (10-715) and Intermediate Statistics (36-705).

AM207: Monte Carlo Methods, Stochastic Optimization^[473]

Introduction: The Monte Carlo Method and Stochastic Optimization course of Harvard University is a graduate course in applied mathematics, taught by V Kaynig-Fittkau and P Protopapas, with Python program examples. If you are interested in Bayesian reasoning, you must check out the lecture video and IPN handout [474].

SPARK Big Data Application in Biomedicine^[475]

Introduction: Biomedical SPARK big data application. Berkeley has opened source their Big Data Genomics system, ADAM[476]. For other information, please visit the official website [477].

“The ACL Anthology”^[478]

Introduction: For those interested in natural language processing or machine translation technology, please do a simple Google Academic search before coming up with your own fantastic idea (automatic induction of translation rules, automatic understanding of context, automatic recognition of semantics, etc.). If Google is not available, This site has a list of the top papers in the field. Don’t take them out of context and make assumptions.

Twitter Sentiment Detection via Ensemble Classification Using Averaged Confidence Scores^[479]

Introduction: Paper + code: Twitter sentiment classification based on integration method, implementation code [480].

NIPS 2014 CIML Workshop^[481]

Introduction :NIPS CiML 2014 PPT,NIPS is the English abbreviation of the Conference on Advances in Neural Information Processing Systems.

Convolutional Neural Networks for Visual Recognition^[482]

Introduction: Deep Learning Projects at Stanford where everyone has to write a paper level report and there are some interesting applications that you can look at.

A Speed Comparison Between Flexible Linear Regression Alternatives in R^[483]

Introduction :R language linear regression multi-scheme speed comparison schemes include LM (), NLS (), GLM (), Bayesglm (), NLS (), MLE2 (), Optim () and Stan’s Optimizing (), etc.

Back-to-basics Weekend Reading-Machine Learning^[484]

Introduction: The three papers mentioned in the article (machine learning things, unsupervised clustering overview, supervised classification overview) are classic, and Domnigos’s machine learning class is excellent

A Probabilistic Theory of Deep Learning^[485]

Introduction: Probabilistic theory of Deep Learning, Rice University.

Nonsensical Beer Reviews via Markov Chains^[486]

An open source Twitter bot that automatically generates beer reviews based on Markov chains,github address [487].

Deep Learning for Natural Language Processing (Without Magic)^[488]

Introduction: Video + Handout: Deep Learning for Natural Language Processing Tutorial (NAACL13).

Introduction to Data Analysis using Machine Learning^[489]

Introduction: Data Analysis with Machine Learning, a recent McGill University seminar presented by David Taylor, also provides a series of IPNs on machine learning methods, very valuable GitHub[490]. Domestic [491]

Beyond Short Snippets: Deep Networks for Video Classification^[492]

Introduction: Video classification based on CNN+LSTM, Google Demo [493].

How does Quora Use Machine Learning in 2015?^[494]

Introduction: How Quora uses machine learning.

Amazon Machine Learning — Making Data-Driven Decisions at Scale^[495]

Introduction: Amazon in machine learning some applications, code examples [496].

Parallel Machine Learning with Scikit-Learn and IPython^[497]

Introduction: Parallel Machine Learning Guide (based on Scikit-learn and IPython). Notebook [498]

Intro to Machine Learning with SciKit-Learn Intro to Machine Learning with SciKit-Learn^[499]

Introduction :DataSchool teaches basic concepts of machine learning.

“DeepCLn”^[500]

Introduction: A convolutional neural network based on OpenGL implementation, support Linux and Windows system.

An Inside Look at the Components of a Recommendation Engine^[501]

A recommendation system based on Mahout and Elasticsearch

Forecasting in Economics, Business, Finance and Beyond^[502]

Introduced: Francis x. Diebold’s (economy | | business finance etc) prediction method.

Time Series Econometrics – A Concise Course^[503]

Diebold, Francis X. Timing Econometrics.

A Comparison of Open Source Tools for Sentiment Analysis^[504]

Introduction: A comparison of open source sentiment analysis tools [505] based on Yelp datasets, reviews cover Naive Bayes, SentiWordNet, CoreNLP, etc.

Pattern Recognition And Machine Learning^[506]

Introduction: Domestic Pattern Recognition And Machine Learning reading club resources summary, each chapter PDF lecture notes [507], blog [508].

Probabilistic Data Structures for Web Analytics and Data Mining^[509]

Introduction: Probabilistic data structures for Web analytics and data mining.

Machine Learning in Navigation Devices: Detect outvers Using Accelerometer and Gyroscope^[510]

Application of machine learning to navigation.

Neural Networks Demystified^[511]

Introduction :Neural Networks Demystified series video, made by Stephen Welch, pure hand-painted style, easy to understand, Domestic cloud [512].

“Swirl + DataCamp”^[513]

R& Data Science interactive online tutorial

“Learning to Read with Recurrent Neural Networks”^[514]

Sequence to Sequence Learning with Neural Networks[515].

Resources for Deep Reinforcement Learning^[516]

Reinforcement Learning of Deep Reinforcement.

Machine Learning with SciKit-Learn^[517]

Parallel Machine Learning with SciKit-Learn and IPython[518]

“PDNN”^[519]

PDNN: A Python Toolkit for Deep Learning

Introduction to Machine Learning^[520]

Introduction: CMU’s spring 15 machine learning course, delivered by Alex Smola, offers handouts and video lectures. Domestic mirror image [521].

Big Data Processing^[522]

Introduction: Big data processing. Content covers stream processing, MapReduce, graph algorithm, etc.

Spark MLlib: Making Practical Machine Learning Easy and Scalable^[523]

Spark MLlib, Spark MLlib, Spark MLlib, Spark MLlib, Spark MLlib

Picture: A Probabilistic Programming Language for Scene Perception^[525]

Introduction: Previous probabilistic programming (language) implementations of thousands of lines of code require only 50 lines.

Beautiful Plotting in R: A Ggplot2 Cheatsheet^[526]

A new Data Processing Workflow for R: Dplyr, Magrittr, Tidyr, GGplot2 [528] is also recommended.

Using Structured Events to Predict Stock Price Movement: An Empirical Investigation^[529]

Introduction: Using structured models to predict real-time stock prices.

International Joint Conference on Artificial Intelligence Accepted Paper^[530]

International Conference on Artificial Intelligence [531]. Most of the papers can be found using Google.

Why GEMM is at the Heart of Deep Learning^[532]

Introduction: The importance of General Matrix multiplication (GEMM) for deep learning.

Distributed (Deep) Machine Learning Common^[533]

A Community of awesome Distributed Machine Learning C++ projects

Reinforcement Learning: An Introduction^[534]

Reinforcement Learning[538]. Reinforcement Learning[538]. Reinforcement Learning[538]. Reinforcement Learning[538].

Free ebook: Microsoft Azure Essentials: Azure Machine Learning^[539]

Introduction: Free Book :Azure ML Usage essentials.

A Deep Learning Tutorial: From Perceptrons to Deep Networks^[540]

A Deep Learning Tutorial: From Perceptrons to Deep Networks

Machine Learning is Fun – The World’s Easiest Introduction to Machine Learning^[541]

Interesting Machine learning: A Concise Guide to Getting started, Chinese version [542].

A Brief Overview of Deep Learning^[543]

Introduction: A concise introduction to Deep Learning, Chinese version [544].

“Wormhole”^[545]

Portable, Scalable and Reliable Distributed Machine Learning

“Convnet – benchmarks”^[546]

Caffe, Torch-7, CuDNN, CUDaconvnet2, FBFFT, Nervana Systems, etc. NervanaSys performed well.

This catalogue Lists resources developed by faculty and Students of the Language Technologies Institute.^[547]

NLP open Source software toolkit, basic data sets, proceedings, data mining tutorials, machine learning resources

Sentiment Analysis on Twitter^[548]

Introduction :Twitter SentiTweet, video + Handout [549].

Machine Learning Repository @Wash U^[550]

Machine Learning Paper Repository at the University of Washington

Machine Learning Cheat Sheet^[551]

Introduction: Machine learning checklist.

Spark Summit East 2015 Agenda^[552]

Description: Latest Spark Summit documents.

Spark Summit East 2015 Agenda^[553]

Description: Latest Spark Summit documents.

The Learning Spark”^[554]

Introduction :Ebook Learning Spark.

Advanced Analytics with Spark, Early Release Edition^[555]

Ebook Advanced Analytics with Spark, Early Release Edition

Chinese Machine Learning Algorithms and Applications: Tang Jie^[556]

Introduction: Associate professor of Tsinghua University, is an expert in graph mining. He presided over the design and implementation of Arnetminer, a leading graph mining system in China and a support provider for multiple conferences.

Chinese Machine Learning Algorithms and Applications: Yang Qiang^[557]

Introduction: International leader in transfer learning.

Chinese Machine Learning Algorithms and Applications: Zhou Zhihua^[558]

Introduction: It is internationally influential in semi-supervised learning, multi-label learning and integrated learning.

Chinese Machine Learning Algorithms and Applications: Haifeng Wang^[559]

Information retrieval, natural language processing, machine translation experts.

Chinese Machine Learning Algorithms and Applications: Wu Jun^[560]

Introduction: Dr. Wu Jun is currently the main designer of Google’s Chinese, Japanese and Korean search algorithm. During his time at Google, he led many research and development projects, including many Chinese-related products and natural language processing projects, his new personal homepage [561].

Cat Paper Collection^[562]

Introduction: A collection of feline related papers.

How to Evaluate Machine Learning Models, Part 1: Orientation^[563]

How to Evaluate Machine Learning Models, Part 2A: Classification Metrics[564],How to Evaluate Machine Learning Models, Part 2b: Ranking and Regression Metrics[565].

Building a New Trends Experience^[566]

Introduction: Basic implementation framework for Twitter’s new Trends.

Storm Blueprints: Patterns for Distributed Real-time Computation^[567]

Introduction :Storm manual, Chinese translation version [568], thank you.

“SmileMiner”^[569]

Java machine learning algorithm library SmileMiner.

Writing Methods and Skills of Machine Translation Academic Papers^[570]

Introduction: Machine translation academic paper writing methods and skills, How to Write a Great Research Paper[572] by Simon Peyton Jones talk[573].

Efficient BP in Neural Network Training with Tricks^[574]

Introduction: Neural network training in Tricks efficient BP, blogger’s other blog is also quite wonderful.

The Story of NLP and ME^[575]

Introduction: The author is a master of NLP direction, in just a few years, the research results are quite abundant, recommend new friends to read.

The H Index for Computer Science^[576]

Introduction: Jens Palsberg of UCLA established an H-index list of outstanding people in the field of computing based on Google Scholar. Most of them are familiar with various fields, including 1 Nobel Prize winner, 35 Turing Prize winners, and nearly 100 academicians of the American Academy of Engineering/Science. More than 300 ACM fellows are recommended here because you can get more resources by searching the names of outstanding people on Google. This information is very valuable.

“Structured Learning for Taxonomy Induction with Belief Propagation”^[577]

Introduction: Use a large corpus to learn hierarchical relationships of concepts, such as bird is superior to parrot, parrot is superior to budgerigar. The innovation lies in the model construction. Factor graph is used to describe the dependency relationship between concepts. Because sibling relationship is introduced, the graph is loopy Propagation, so marginal probability is calculated iteratively by loopy propagation.

The Bayesian analysis”^[578]

Introduction: This is a bayes analysis of commercial software, the official written Bayes analysis manual [579] has more than 250 pages, although R language has a similar project [580], but after all, can add an option.

Deep Net Highlights from 2014^[581]

Introduction: Deep Net Highlights from 2014.

“The Fast R – CNN”^[582]

Proposes Fast R-CNN, a clean and Fast framework for object Detection.

Fingerprinting Images for Near-Duplicate Detection^[583]

Introduction: Image fingerprint repeated recognition, author source [584], domestic translation version [585].

The Computer Vision Industry^[586]

Company information on computer vision and machine vision applications. Areas of application include: automatic auxiliary driving and traffic management, eye and head tracking, video motion analysis, film and television industry, gesture recognition, general visual system, all kinds of industrial automation and inspection, pharmaceutical and biotechnology, mobile devices, target recognition and AR, people tracking, camera, security monitoring, biological monitoring, three-dimensional modeling, web and cloud applications.

Seaborn: Statistical Data Visualization^[587]

Visual statistics open Source library for Python.

IPython Lecture Notes for OCW MIT 18.06^[588]

Introduction: Notes on Gilbert Strang Linear Algebra at MIT, Video on Gilbert Strang Linear Algebra at MIT [589]

Canova: A Vectorization Lib for ML^[590]

Introduction: Canova, Github [591], a data vectorization tool for machine learning/deep learning, supports CSV files, MNIST data, TF-IDF/Bag of Words/ word2VEc text vectorization.

DZone Refcardz: Distributed Machine Learning with Apache Mahout^[592]

Quick Start: Distributed machine learning based on Apache Mahout.

Learning SciKit-Learn: Machine Learning in Python^[593]

Scikit-learn introduces some machine learning techniques, such as SVM, NB, PCA, DT, feature engineering, feature selection and model selection.

Lightning Fast Machine Learning with Spark^[594]

Introduction: Spark based Efficient Machine Learning, video address [595].

How We’re Using Machine Learning to Fight Shell Selling^[596]

Introduction :WePay uses machine learning to fight “shell selling” fraud on credit cards.

Data Scientists Thoughts That Inspired Me^[597]

Introduction: Selected quotes from 16 data scientists.

Deep Learning Applications and Challenges in Big Data Analytics^[598]

Introduction: Applications and challenges of deep learning in big data analysis.

Free Book :Machine Learning,Mathematics

Free books on machine learning and mathematics, as well as other free books on programming languages, design, operating systems, etc.

“Object Detection via a Multi-Region & Semantic segmentation- Aware CNN Model”^[600]

CNN model object recognition Paper.

A Statistical View of Deep Learning (V): Generalisation and Regularisation^[601]

Introduction: Statistical analysis of deep learning V: Generalization and regularization.

The Highway Networks”^[602]

Introduction: Large scale (multilayer) deep network HN for efficient training with SGD.

What I Read For Deep-learning^[603]

Introduction: Deep learning to interpret articles.

An Introduction to Recommendation Engines^[604]

Recommender Systems Introduction to Recommender Systems

Stanford Machine Learning^[605]

Notes on classic Machine Learning by Andrew Ng.

“ICLR 2015”^[606]

ICLR 2015 Insights and other machine learning articles on blog [607] are also good.

Stanford Machine Learning^[608]

Introduction: “personalized semantic ordering” model of recommendation system.

The More Excited We Are, The Shorter We Tweet^[609]

Introduction: Watch your Words when You’re Passionate — MIT’s latest Twitter study.

Home page of Research Papers on Human Language Technology of Soochow University^[610]

Introduction: Paper on human language technology research of Soochow University.

Neural Turing Machines Implementation^[611]

Introduction: Implementation of neural Turing Machine (NTM), project address [612], in addition to the recommendation of the relevant neural Turing machine algorithm [613].

Computer Vision – CSE 559A, Spring 2015^[614]

Introduction: Machine Vision, University of Washington (2015), Computer Vision: Algorithms and Applications[615]

Mining of Massive Datasets^[616]

“Mining of Massive Datasets” is released with Jure Leskovec, Anand Rajaraman, And Jeff Ullman as co-authors. Three new chapters on social graph data mining, scaling and large-scale machine learning have been added. The electronic version [617] remains free.

Learning Deep Learning^[618]

Introduction: a deep learning resource page, information is very rich.

Learning Deep Learning^[619]

Introduction: Free ebook “Learning Deep Learning”.

Tutorial: Machine Learning for Astronomy with SciKit-Learn^[620]

Machine Learning for Astronomy with Scikit-learn

An Introduction to Random Forests for Beginners^[621]

Introduction: Free ebook “Random Forest Primer “.

Top 10 Data Mining Algorithms in Plain English^[622]

Introduction: Vernacular data mining ten algorithms.

An Inside Look at the Components of a Recommendation Engine

A recommendation system based on Mahout and Elasticsearch [623]

Advances in Extreme Learning Machines^[624]

Introduction: Doctoral dissertation: Progress of ELM research.

10-Minute Tour of Pandas^[625]

Pandas ten-minute overview, IPN [626]

Data Doesn’t Grow in Tables: Harvested Journalistic Insight from Documents^[627]

Introduction: Text Mining for data news.

Time-lapse Mining from Internet Photos^[628]

Introduction: Synthesizing time-lapse video with Web Images (SIGGRAPH 2015).

The Curse of Dimensionality in Classification^[629]

Introduction: Classification system dimension disaster.

Deep Learning vs Big Data: Who Owns What?^[630]

Introduction: Deep Learning vs. Big Data — From Data to Knowledge: Reflections on Copyright,[translated](www.csdn.net/article/201… [631]

A Primer on Predictive Models^[632]

Introduction to predictive modeling.

Demistifying LSTM Neural Networks^[633]

Introduction: Simple LSTM.

“ICLR 2015”^[634]

Introduction: ICLR 2015 conference video [635] and handout [636].

On Visualizing Data Well^[637]

Introduction: Data visualization advice from Ben Jones.

Decoding Dimensionality Reduction, PCA and SVD^[638]

Introduction: Interpreting data dimension reduction /PCA/SVD.

“Supervised Learning Steps Cheat Sheet”^[639]

IPN: Examples of supervised learning methods/comparative reference table, covering Logistic regression, decision tree, SVM, KNN, Naive Bayes, etc.

DopeLearning: A Computational Approach to Rap Lyrics Generation^[640]

Introduction: Based on RankSVM and DNN automatic (reorganization) generation of Rap lyrics.

An Introduction to Random Indexing^[641]

Introduction: Topics on random index RI lexical space Models.

“VDiscover”^[642]

Introduction: Vulnerability detection tool VDiscover based on machine learning.

“Minerva”^[643]

Minerva, a deep learning system. Have a Python programming interface. Multiple Gpus achieve almost linear acceleration. GoogLeNet can be trained to 68.7% top-1 accuracy and 89.0% top-5 accuracy within 4 days on 4 Gpus. Compared with CXXNET, which is also a DMLC project, the dynamic data flow engine provides more flexibility. In the future, it will integrate with CXXNET as MXNET project to gain mutual advantages.

CVPR 2015 Paper^[644]

2015 International Conference on Computer Vision and Pattern Recognition

What are the Advantages of Different Classification Algorithms?^[645]

Introduction: Classification algorithm in Netflix engineering Director’s eyes: Deep learning has the lowest priority, Chinese version [646].

Results for Microsoft COCO Image Captioning Challenge

Introduction :Codalab image labeling contest ranking + various papers, flukeskywalker on Reddit collated various technology-related papers [647].

Caffe Con Troll: Shallow Ideas to Speed Up Deep Learning^[648]

Introduction: Accelerated deep learning system CcT based on Caffe.

Low Precision Storage for Deep Learning^[649]

Introduction: Deep learning (modeling) low precision (training and) storage.

Model-based Machine Learning (Early Access)^[650]

Introduction: New book Preview: Model Machine Learning.

Regret Analysis of Stochastic and Nonstochastic Multi-Armed Bandit Problems^[651]

Introduction: Moreover, Introduction to Field: Algorithms and Theory[652].

Kaggle R Tutorial on Machine Learing^[653]

Interactive R Tutorial: Kaggle’s Titanic Competition Interactive R Tutorial: Machine Learning for the Titanic Competition [654].

Deep Learning Learning Notes Collection Series^[655]

Introduction :Deep Learning Learning notes collection series.

Introduction to Neural Machine Translation with GPUs^[656]

Introduction: Introduction to neural (perceptual) machine translation.

Andrew Ng: Deep Learning, Self-taught Learning and Unsupervised Feature Learning^[657]

Introduction :Andrew Ng’s report on deep Learning/Self-learning/Unsupervised Feature Learning, China Cloud [658].

“Recurrent Neural Network Training with Dark Knowledge Transfer”^[659]

Paper: Training RNN by latent knowledge transfer.

Show Me The Money^[660]

Emotion Analysis tool for financial data.

“PyLDAvis”^[661]

Introduction :(Python) theme model interaction visualization library pyLDAvis.

Logistic Regression and Gradient Descent^[662]

Introduction :Logistic regression and optimization example tutorial.

Transcript of Jia Yangqing’s wechat Lecture^[663]

Introduction: Wechat lecture notes by Jia Yangqing, Google brain scientist and founder of Caffe.

“Sketch”^[664]

Theano/Blocks implements RNN handwritten string generation with Sketch.

Web Scale Document Clustering: Clustering 733 Million Web Pages^[665]

Introduction: Massive (700 million +) web page clustering based on TopSig.

NAACL 2015 Proceedings on ACL Anthology^[666]

Introduction :NAACL 2015 Paper Papers.

Stock Forecasting With Machine Learning – Seven Possible Errors^[667]

Seven questions about machine Learning to Predict the stock market.

Are There Any Good Resources for Learning About Neural Networks?^[668]

Introduction: Neural network learning materials recommended.

“A Critical Review of Recurrent Neural Networks for Sequence Learning”^[669]

Introduction: A review of RNN for sequence learning.

Handling and Processing Strings in R^[670]

Introduction :R text processing manual.

Must-watch Videos About Python^[671]

Introduction: “Must See” Python video collection.

The Google Stack^[672]

Introduction :Google(Infrastructure) stack.

Randomized Algorithms for Matrices and Data^[673]

Introduction: Random algorithms for Matrices and data (UC Berkeley 2013).

The Intermediate R^[674]

Introduction :DataCamp Intermediate R tutorial.

Topology Without Tears^[675]

Introduction: Free ebook: Easy to master Topology, Chinese version [676].

Information Theory, Pattern Recognition, and Neural Networks^[677]

Introduction: the Book [678], video [679].

“Scikit – learn”^[680]

Scikit-learn is a Python module based on Scipy for machine learning. It features a variety of classification, regression and clustering algorithms, including support vector machines, logistic regression, Naive Bayes Classifier, Random forest, Gradient Boosting. Clustering algorithm and DBSCAN. Python numerical and Scientific Libraries Numpy and Scipy have also been designed

“Pylearn2”^[681]

Pylearn is a Theano-based library that simplifies machine learning research.

“NuPIC”^[682]

NuPIC is a machine intelligence platform based on HTM learning algorithm. HTM is an accurate method for calculating the cortex. The core of HTM is the time-based continuous learning algorithm and the space-time pattern of storage and revocation. NuPIC is suitable for a wide variety of problems, especially for detecting anomalies and predicting stream data sources.

“Nilearn”^[683]

Nilearn is a Python module that enables rapid statistical learning of neuroimaging data. It utilizes Python’s SciKit-Learn toolkit and applications for predictive modeling, classification, decoding, and connectivity analysis to perform multivariate statistics.

“PyBrain”^[684]

Pybrain is a Python based reinforcement learning, Artificial Intelligence, neural network library. Its goal is to provide flexible, easy to use, and powerful machine learning algorithms and compare your algorithms with tests in a variety of predefined environments.

“The Pattern”^[685]

Pattern is a Python network mining module. It provides tools for data mining, natural language processing, network analysis and machine learning. It supports vector space models, clustering, support vector machines and perceptrons and is classified by KNN classification.

“Arg”^[686]

Fuel provides data for your machine learning model. He has an interface for sharing datasets such as MNIST, CIFAR-10 (image dataset), and Google’s One Billion Words (text). You use it to substitute your own data in a variety of ways.

“Bob”^[687]

Bob is a free signal processing and machine learning tool. Its toolkit, written in Python and C++, is designed to be more efficient and reduce development time, and is made up of numerous software packages for processing image tools, audio and video processing, machine learning, and pattern recognition.

“Skdata”^[688]

Skdata is a library for machine learning and statistical data sets. This module provides use of the standard Python language for toy problems, popular computer vision and natural language datasets.

“MILK”^[689]

MILK is a Python machine learning toolkit. It mainly uses supervised taxonomy in many available classifications such as SVMS,K-NN, random forest, and decision tree. It also performs feature selection. The combination of these classifiers in many aspects can form different classification systems such as unsupervised learning, close relationship gold propagation and K-means clustering supported by MILK.

“IEPY”^[690]

IEPY is an open source information extraction tool that focuses on relationship extraction. It is aimed at users who need to extract information from large data sets and scientists who want to try out new algorithms.

“Quepy”^[691]

Quepy is a Python framework for changing natural language problems to make queries in the database query language. It can simply be defined as different types of problems in natural language and database queries. So, you can build your own system for accessing your database in natural language without coding. Quepy now provides support for Sparql and MQL query languages. There are plans to extend it to other database query languages.

“Hebel”^[692]

Hebel is a Python library for deep learning of neural networks. It uses PyCUDA to accelerate GPU and CUDA. It is the most important type of neural network model tool and can provide activation functions for several different activity functions, such as dynamic, Nesterov dynamic, signal loss and stop method.

“Mlxtend”^[693]

It is a library of useful tools and extensions for everyday data science tasks.

“Nolearn”^[694]

Introduction: This package contains a number of utility modules that can help you accomplish machine learning tasks. Many of these modules work with SciKit-Learn, while others are often more useful.

The Ramp^[695]

Ramp is a Python library for developing solutions to speed up prototyping in machine learning. Ramp is a lightweight pluggable framework for pandas based machine learning. Its existing Python machine learning and statistics tools (sciKit-learn, RPY2, etc.), Ramp, provide a simple declarative syntax exploration to implement algorithms and transformations quickly and efficiently.

The Feature Forge”^[696]

Scikit-learning-compatible apis for creating and testing machine learning capabilities. This library provides a set of tools that you’ll find useful in many machine learning programs. When you use the SciKit-learn tool, you will feel greatly helped. (Although this only works if you have a different algorithm.)

The REP^[697]

REP is an environment for directing data movement drives in a harmonious and renewable manner. It has a unified classifier wrapper to provide various operations, such as TMVA, Sklearn, XGBoost, uBoost, and so on. And it can train classifiers in parallel in a population. It also provides an interactive plot.

Sample Python Learning Machine^[698]

Introduction: Simple software collection built with Amazon’s machine learning.

The Python – ELM^[699]

This is an implementation of an extreme learning machine based on Scikit-learn in Python.

The Dimension Reduction”^[700]

Dimensionality Reduction A Short Tutorial[701], Matlab Toolbox for Dimensionality Reduction[702], Unsupervised Kernel Dimension Reduction[703]

Datasets Used For Benchmarking Deep Learning Algorithms^[704]

A list of deeplearning datasets compiled by Deeplearning.net.

Golang Natural Language Processing^[705]

Introduction :Go language written natural language processing tools.

“Rehabilitation of Count-based Models for Word Vector Representations”^[706]

Improving Distributional Similarity with Lessons Learned from Word Embeddings [707].

Three Aspects of Predictive Modeling^[708]

Introduction: Three aspects of forecasting models.

CS224d: Deep Learning for Natural Language Processing^[709]

Introduction: Stanford University Deep Learning and Natural Language Processing course, Part of the course notes word Vector [710], introduction [711]

Google Computer Vision Research at CVPR 2015^[712]

Introduction: Google list of CV studies on CVPR2015.

Using Deep Learning to Find Basketball Highlights^[713]

Metamind: Deep learning for automatic discovery of basketball highlights.

Learning Deep Features for Discriminative Localization^[714]

Introduction: Analysis of localization feature learning

“Image Scaling Using Deep Convolutional Neural Networks”^[715]

Introduction: Image scaling using convolutional neural networks.

Proceedings of The 32ND International Conference on Machine Learning^[716]

Introduction :ICML2015 Proceedings, 4 optimization + 1 sparse optimization; 4 reinforcement learning, 3 deep learning + 1 deep learning calculation; Bayes nonparametric, Gaussian process and learning theory; Computing advertising and social choice. ICML2015 Sessions[717].

“Image Scaling Using Deep Convolutional Neural Networks”^[718]

Introduction: Image scaling using convolutional neural networks.

Microsoft Researchers Accelerate Computer Vision Accuracy and Improve 3D Scanning Models^[719]

The 28th IEEE Computer Vision and Pattern Recognition (CVPR) conference was held in Boston, USA. Microsoft researchers at the conference showed off a new model for classifying computer vision images faster and more accurately than ever before, and described how sensors such as Kinect can be used for fast, large-scale 3D scanning in dynamic or low-light environments.

Machine Learning for Humans^[720]

Introduction: Machine learning visual analysis tools.

A Plethora of Tools for Machine Learning^[721]

Introduction: Overview/comparison of machine learning toolkits/libraries.

The Art of Visualizing VisualIzations: A Best Practice Guide^[722]

Introduction: Best practice guide for Data Visualization.

MIT Machine Learning for Big Data and Text Processing Class Notes – Day 1^[723]

Description :Day 1[724], Day 2[725], Day 3[726], Day 4[727], Day 5[728].

Getting “deep” About “Deep Learning”^[729]

Introduction: The “depth” of deep Learning — METAPHOR Analysis of DNN.

Mixture Density Networks^[730]

Introduction: Mixed density networks.

Interview Questions for Data Scientist Positions^[731]

Introduction: Data scientist job interview questions.

“Accurately Measuring Model Prediction Error”^[732]

Accurately evaluate model prediction errors.

Notebooks of Continually Updated Data Science Python^[733]

Notebooks of Data Science Continuously updated.

“How to Share data with a Statistician”^[734]

Statistician How to share data with a statistician.

The Eyescream Project NeuralNets Dreaming Natural Images^[735]

Introduction: Automatic image generation from Facebook.

“How to Share data with a Statistician”^[736]

Statistician How to share data with a statistician.

A Neural Conversational Model^[737]

Introduction :(Google) neural (perceptual) conversation model.

The 50 Best Masters in Data Science^[738]

The 50 Best Masters in Data Science

NLP Common Information Resources^[739]

Description: Common NLP information resources.

Conditional Random Fields as Recurrent Neural Networks^[740]

Introduction: Live demonstration of semantic image segmentation [741], through deep learning techniques and probabilistic graph model of semantic image segmentation.

Fully Convolutional Networks for Semantic Segmentation^[742]

Caffe model/code: Full convolutional network for image semantic segmentation, model code.

Growing Pains for Deep Learning^[743]

Deep learning: Growing pains.

Clustering Text Data Streams — A Tree Based Approach with Ternary Function and Ternary Feature Vector^[744]

Text stream clustering based on ternary tree method.

Foundations and Advances in Data Mining^[745]

Free Ebook: Data Mining basics and latest progress.

The Deep Learning Revolution: Rethinking Machine Learning Pipelines^[746]

Introduction: The Deep learning revolution.

Definitive Guide to Do Data Science for Good^[747]

Introduction: Authoritative Guide to data Science (practice).

Microsoft Academic Graph^[748]

Introduction: 37GB Microsoft Academic Atlas dataset.

Challenges and Opportunities Of Machine Learning In Production^[749]

Introduction: Opportunities and challenges for machine learning in production environments (product-level).

Neural Nets for Newbies^[750]

Introduction to neural networks.

A Nearly Linear Time Framework for Graph-Structured Sparsity^[751]

Introduction: Structured sparse papers from MIT.

Optimal and Adaptive Algorithms for Online Boosting^[752]

Boosting Online By the Machine Learning group at Yahoo!

Top 20 Python Machine Learning Open Source Projects^[753]

Introduction :20 of the hottest Open Source (Python) machine learning projects.

The Parallel C++ Statistical Library for Bayesian Inference: QUESO^[754]

C++ parallel bayesian inference statistical library QUESO,github code[755].

Deep Learning Yann LeCun, Yoshua Bengio, Geoffrey Hinton (2015)^[756]

Introduced: Nature: LeCun Bengio/the latest articles “Deep Learning” Hinton, Jurgen Schmidhuber latest commentary “Critique of Paper by” Deep Learning Conspiracy” Nature 521 p 436 [757].

The Palladium^[758]

Introduction: Palladium, a predictive analysis service framework based on SciKit-Learn

Advances in Structured Prediction^[759]

Introduction :John Langford and Hal Daume III’s instructional lecture slides on Learning to Search at ICML2015.

100 Open Source Big Data Architecture Papers for Data Professionals^[760]

After reading these 100 papers, you can become a master of big data.

Social Media & Text Analytics^[762]

Introduction: Selected reading list for NLP course Social Media and Text Analysis.

Machine Learning for Developers^[763]

Introduction: Machine learning Guide for developers.

Hot News Detection Using Wikipedia^[764]

Introduction: Hot News Discovery based on Wikipedia.

Harvard Intelligent Probabilistic Systems Group^[765]

Introduction :(Harvard)HIPS will release scalable/auto-tuned bayesian inference neural networks.

An Empirical Exploration of Recurrent Network Architectures^[766]

Introduction: Hierarchical recursive codec for context-aware query suggestions.

Efficient Training of LDA on a GPU by Mean-for-Mode Estimation^[767]

Introduction: Efficient LDA training on GPU based on Mean-for-mode estimation.

From the Lab to the Factory: Building a Production Machine Learning Infrastructure^[768]

Introduction: From Lab to Factory — Building machine Learning Production Architectures.

6 Useful Databases to Dig for Data (and 100 more)^[769]

Introduction: Six classic data sets (and another 100 lists) suitable for data mining.

Deep Networks for Computer Vision at Google — ILSVRC2014^[770]

Google deep Learning for Machine Vision.

How to Choose a Machine Learning API to Build Predictive Apps^[771]

Introduction: How to choose machine learning apis when building predictive applications.

Exploring the Shapes of Stories using Python and sentiment APIs^[772]

Introduction :Python+ Sentiment Analysis API for storyline (curve) analysis.

Movie Selection Using R^[773]

Introduction :(R) word-of-mouth movie recommendation based on Twitter/ sentiment analysis, and empirical comparative analysis of recommendation classification algorithms [774].

Tutorial on Graph-based Semi-supervised Learning Algorithms for NLP^[775]

Introduction :CMU(ACL 2012)(500+ pages) Graph-based semi-supervised Learning algorithms for NLP.

Arbitrariness of Peer Review: A Bayesian Analysis of the NIPS Experiment^[776]

Introduction: The significance of peer review from bayesian analysis of NIPS.

Basics of Computational Reinforcement Learning^[777]

Introduction :(RLDM 2015) introduction to computational reinforcement learning.

Deep Reinforcement Learning^[778]

Introduction: Deep Reinforcement Learning by David Silver.

On Explainability of Deep Neural Networks^[779]

Introduction: Interpretability of deep neural networks.

The Essential Spark Cheat Sheet^[780]

Introduction :Spark Quick Start.

Machine Learning for Sports and Real Time Predictions^[781]

TalkingMachines: machine learning for sports/politics and real-time prediction.

CS224W: Social and Information Network Analysis Autumn 2014^[782]

Introduction :Stanford Social Network and Information Network Analysis course Materials [783]+ course design [784]+ Data [785]

RL Course by David Silver^[786]

Introduction: Reinforcement Learning course by David Silver(DeeMind), Slide [787].

Faster Deep Learning with GPUs and Theano^[788]

Efficient deep Learning based on Theano/GPU.

Introduction to R Programming^[789]

Introduction: From Microsoft.

Golang:Web Server For Performing Sentiment Analysis^[790]

Sentiment Server :(Go) Sentiment analysis API.

A Beginner’s Guide to Restricted Boltzmann Machines^[791]

Introduction: Restricted Boltzmann machine beginner’s guide.

KDD2015 best papers of the decade^[792]

Introduction :Mining and Nb Customer Reviews [793],Mining High-speed Data Streams[794],Optimizing Search Engines Using Clickthrough Data[795].

Nvidia Deep Learning Courses^[796]

Nvidia Deep Learning Course

Deep Learning Summer School 2015^[797]

Introduction :2015 Deep Learning Summer Course, recommended lecturer homepage [798].

Progress of Image Recognition in Baidu Deep Learning^[799]

Introduction: This is an abstract of baidu’s article “Progress in Image Recognition based on deep learning: Some practices of Baidu” [800]. It is suggested to read the two articles together.

Machine Learning Methods in Video Annotation^[801]

Introduction: Machine learning techniques in video tagging.

Training Recurrent Neural Networks^[802]

Introduction: PhD thesis :(Ilya Sutskever)RNN training.

On Explainability of Deep Neural Networks^[803]

Introduction: Grey Regions of Deep Neural Networks: Interpretability problems, Chinese edition [804].

Machine Learning Libraries in GoLang by Category^[805]

Golang implements machine learning library resources summary.

A Statistical View of Deep Learning^[806]

Introduction: Statistical analysis of deep learning.

Deep Learning For NLP – Tips And Techniques^[807]

Deep learning techniques and techniques for NLP.

CrowdFlower Competition Scripts: Approaching NLP^[808]

Introduction :Kaggle’s CrowdFlower Contest NLP code collection.

CS224U: Natural Language Understanding^[809]

Introduction: Stanford’s Natural Language Understanding program.

Deep Learning and Shallow Learning^[810]

Deep Learning and Shallow Learning

A First Encounter with Machine Learning^[811]

Max Welling[812] is a machine learning ebook written by Max Welling[812], who has extensive experience in machine learning teaching.

Click Models for Web Search^[813]

Introduction: By University of Amsterdam, Netherlands & Google Switzerland.

“Hinton CSC321 course/Deep/Python/Learning/Notes on CNN Theano/CUDA/OpenCV /…”^[814]

I am interested in summarizing and translating machine learning and computer vision materials. I am interested in summarizing Hinton’s CSC321 course. Deep Learning; Notes on CNN summary; Summary of python principles; Summary of Theano basic knowledge and exercises; CUDA principles and programming; Some summary of OpenCV.

Which Algorithm Family Can Answer My Question?^[815]

Introduction: How to select machine learning algorithms for specific problems (application scenarios) (series).

Free Data Science Books^[816]

Free books on data Science

Tutorial 4: Deep Learning for Speech Generation and Synthesis^[817]

Introduction: What is the latest progress of deep learning in speech synthesis? Recommended videos and slides of MSRA teacher Frank Soong on deep learning method of speech synthesis and Introduction of Google’S LSTM-RNN synthesis [818], paper [819]

The Art of Data Science^[820]

Introduction: New book (free download): The Art of Data Science

Pattern Recognition and Machine Learning^[821]

Pattern Recognition and Machine learning is one of the most widely recognized books on machine learning, written by Bishop Of Microsoft Research Cambridge. This book covers a wide range of topics and is suitable for graduate students.

An Introduction to Visualizing DATA^[824]

Introduction: Introduction to Data Visualization (23 page pocket booklet)

“That’s So Annoying!!!!!! : A Lexical and Frame-Semantic Embedding Based Data Augmentation Approach to Automatic Categorization of Annoying Behaviors Using #petpeeve Tweets ∗^[825]

Introduction: This paper was awarded the EMNLP2015 Best Data/Resource Award for Excellence, annotated Twitter Datasets [826]

26 Things I Learned in the Deep Learning Summer School^[827]

Introduction: The author’s thoughts on deep learning.

Data-visualization Tools & Books^[828]

This section describes the software resources of common data visualization tools

Machine Learning and Probabilistic Graphical Models Course^[829]

Machine Learning and Probabilistic Graph Modeling by Sargur Srihari at the University of Buffalo

Understanding Machine Learning: From Theory to Algorithms^[830]

Understanding Machine Learning by Shai Shalev-Shwartz, Professor of Hebrew University of Jerusalem, and Shai Ben-David, Professor of University of Waterloo From Theory to Algorithms, machine learning, Algorithms, Algorithms, Algorithms

Machine Learning Checklist^[831]

Introduction: Machine learning learning checklist

Who are the NLP gods?^[832]

Introduction: Zhihu above an article about the NLP community which god level figures? Ask questions. First Michael Collins,

A Gentle Guide to Machine Learning^[833]

Machine learning and NLP expert Raul Garreta, co-founder and CEO of MonkeyLearn, provides an overview of important concepts, applications, and challenges in using machine learning for beginners.

Gradient Return Trees^[834]

Introduction :(IPN) GBRT(Gradient Boost Regression Tree) tutorial based on scikit-learn, slide[835]

Apache SINGA: Distributed Deep Learning System^[836]

Distributed deep learning software that can be used without deep learning.

E-commerce Recommendation with Personalized Promotion^[837]

On Amazon Data and crowd-sourcing Mechanical Turk, mechanisms from lotteries and auctions are implemented to collect training sets of users’ willing to buy prices (WTP) for products. E-commerce Recommendation with Personalized Promotion [Zhao,RecSys15] Regression model predicts unknown WTP and improves seller’s profit and consumer satisfaction

Scalable Machine Learning^[838]

Introduction: Large-scale machine learning from Berkeley.

Summary of Machine Learning Materials^[839]

Introduction: Machine learning data from 52ML.

The Automatic Summarization,^[840]

Introduction: McKeown[841], the author of this book, is the 2013 director of the world’s first Data Science Academy (located at Columbia University), and she is a Fellow of ACL, AAAI, and ACM.

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing^[842]

Introduction: Several text summaries of EMNLP-15.

Recommender Systems (Machine Learning Summer School 2014 @ CMU)^[843]

Introduction: Netflix’s Xavier Amatriain’s 4-hour Summer School 2014 @ CMU report, 248 pages in length, is a comprehensive overview of the development of recommendation systems, including some of Netflix’s experiences with personalized recommendations.

BigData Stream Mining^[844]

Introduction :(ECML PKDD 2015) big data stream mining Tutorial. In addition, the list of ECML PKDD 2015 tutorials is recommended [845].

Deep Learning on Spark with Keras^[846]

Introduction: Elephas, the Keras[847] deep learning framework on Spark.

Prof. Surya Ganguli – The Statistical Physics of Deep Learning^[848]

Introduction :Surya Ganguli deep Learning statistical Physics.

(Systems/Algorithms/Machine Learning/Deep Learning/Graph Models/Optimization /…)^[849]

Introduction :(systems/algorithms/machine learning/deep learning/graph models/optimization /…) List of online video courses.

Introduction to Topic Modeling in Python^[850]

Introduction to :(PyTexas 2015)Python topic modeling.

Large Scale Distributed Deep Learning on Hadoop Clusters^[851]

Introduction: Large-scale distributed machine learning on Hadoop clusters.

Top Deep Learning Employers Based On LinkedIn Data^[852]

Introduction: The top “employers” for deep learning based on LinkedIn data.

Neural Net in C++ Tutorial^[853]

Introduction :(c++) neural network manual implementation tutorial.

“Large-scale CelebFaces Attributes (CelebA) Dataset”^[854]

“Large-scale CelebFaces Attributes (CelebA) Dataset 10K celebrity, 202K face images, each image more than 40 annotation Attributes” was published by The laboratory of Professor Tang Xiaoou of the Chinese University of Hong Kong.

Unsupervised Feature Learning in Computer Vision^[855]

Introduction: Unsupervised Characteristic Learning for Machine Vision,Ross Goroshin’s webpage[856].

Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks^[857]

Introduction: Google Researcher Samy Bengio et al recently written RNN Scheduled Sampling training method paper.

Essential Machine Learning Algorithms in a Nutshell^[858]

Introduction: A brief introduction to basic machine learning algorithms.

A Huge List of Machine Learning And Statistics Repositories^[859]

Github Machine Learning/mathematics/statistics/visualization/Deep Learning related projects

Information Processing and Learning^[860]

Introduction: Information Theory courses at CMU.

Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks^[861]

Introduction: Google Research Institute Samy Bengio[862] et al., a recent paper on RNN Scheduled Sampling training method.

“Large-scale Distributed Deep Learning based on Hadoop Clusters”^[863]

Introduction: Large-scale distributed deep learning based on Hadoop cluster.

Learning Both Weights and Connections for Efficient Neural Networks^[864]

Introduction: Work from Stanford university and NVIDIA, very practical. The parameters of CNN model can be greatly reduced by cutting network connection and retraining. For AlexNet, VGG and other models and ImageNet data, the model parameters can be significantly reduced by 9-13 times without loss of recognition accuracy.

Apache Singa –A General Distributed Deep Learning Platform^[865]

Github [866] is a distributed deep learning software that can be used without deep learning.

24 Ultimate Data Scientists To Follow in the World Today^[867]

The top 25 big data scientists in the world today, by their names and then put in a Google search will be found a lot of great resources.

Deep Learning for NLP – Lecture October 2015^[869]

Nils Reimers’ Deep Learning (Theano/Lasagne) series for NLP.

Connection between Probability Theory and Real Analysis^[870]

Introduction: The speaker is Tao Zhexuan [871], information Probability: Theory and Examples[872], notes [873].

Data Science Learning Resources

Introduction: List of data science (learning) resources.

8 Tactics to Combat Imbalanced Classes in Your Machine Learning Dataset^[874]

Introduction: Eight strategies for solving the problem of unbalanced data set classification.

Top 20 Data Science MOOCs^[875]

Introduction: highlights of 20 data science related courses.

“Recurrent Neural Networks”^[876]

Introduction: Recursive neural networks.

Histograms of Oriented Gradients^[877]

Introduction :(HOG) study notes.

Computational Modelling courses^[878]

Introduction: Summary of computational modeling/Computational Neuroscience courses.

How We Use Deep Learning to Classify Business Photos at Yelp^[879]

Introduction :(Yelp) business photo classification based on deep learning.

Neural Networks and Deep Learning^[880]

Neural Networks and Deep Learning Neural Networks and Deep Learning The first chapter [881] introduces NN through the example of handwritten number recognition, the second chapter discusses the back propagation algorithm, the third chapter discusses the optimization of the back propagation algorithm, and the fourth chapter explains why NN can fit arbitrary functions. Lots of Python code examples and interactive animations, lively and interesting. The Chinese version of [882]

Books to Read If You Might Be Interested in Data Science^[883]

Introduction: Data science recommended books (introduction).

Deep Learning for NLP Resources^[884]

Introduction :NLP deep learning resource list.

“GitXiv”^[885]

Introduction: Many of the best-known arXiv papers can be found on github’s project links.

Learning Multi-Domain Convolutional Neural Networks for Visual Tracking^[886]

Deep learning in visual tracking.

Beginners Guide: Apache Spark Machine Learning Scenario With A Large Input Dataset^[887]

Introduction :Spark machine Learning Example: Big Data Set (30+ G) dichotomy.

The Semantic Scholar”^[888]

Introduction: According to Paul Allen ARTIFICIAL Intelligence Lab, Google Scholar was a product of ten years ago, and they now want to improve it even further. So we launched a new Semantic Scholar, an academic search engine specifically designed for scientists.

The Semi – Supervised Learning,^[889]

Introduced: a semi-supervised learning, Chapelle. Sylvia is classic, the author includes Vapnik, Bengio, Lafferty, Jordan. In addition, Introduction to Semi-supervised Learning[891] by Xiaojin (Jerry) Zhu[890] is recommended.

Introduction :Spark machine Learning Example: Big Data Set (30+ G) dichotomy.

Free Resources for Beginners on Deep Learning and Neural Network^[892]

Introduction: Free resources on deep learning and neural networks for beginners.

TensorFlow is an Open Source Software Library for Machine Intelligence^[893]

White Paper of TensorFlow 2015[894].hacker News [895] : TensorFlow[896]

Veles:Distributed Machine Learning Platform^[897]

Samsung open Source distributed platform for fast deep learning application development.

DMTK:Microsoft Distributed Machine Learning Tookit^[898]

Distributed Machine Learning Toolkit.

Semantics Approach to Big Data and Event Processing^[899]

Introduction: Semantic Big Data — Semantic approaches to big data/event processing.

“LSTM(Long Short Term Memory) and RNN(Recurrent) Learning Course”^[900]

LSTM(Long Short Term Memory) and RNN(Recurrent) learning courses.

Marvin: A Minimalist GPU-Only N-dimensional ConvNet Framework^[901]

Introduction :Princeton Vision Group’s open source deep learning library

Ufora is an Compiled, Automatically Parallel Subset of Python for Data Science and Numerical Computing^[902]

Why I Open At Five Years of Work[903] Ufora, an AWS based automated distributed scientific computing library

Deep Learning and Deep Data Science – PyCon SE 2015^[904]

Introduction :(PyCon SE 2015) deep learning and deep data science.

Zhi-hua Zhou Papers^[905]

Google Academic homepage for Prof. Zhou Zhihua, director of Institute of Machine Learning and Data Mining, Nanjing University

Advanced Linear Models for Data Science^[906]

Introduction: Free book: Advanced Linear Models for Data Science.

Net2Net: Accelerating Learning via Knowledge Transfer^[907]

Introduction: Knowledge transfer based neural network efficient training Net2Net.

Xu Yida Machine Learning Course Variational Inference^[908]

Machine Learning course Variational Inference

Learning the Architecture of Deep Neural Networks^[909]

Deep neural network structure learning.

Multimodal Deep Learning^[910]

Multimodal Deep Learning Papers from Stanford University

The deep learning saving-energy-and-saving-land TensorFlow, Torch, Theano, Mxnet”^[911]

Introduced: deep learning saving-energy-and-saving-land TensorFlow, Torch, Theano, Mxnet [912].

Notes Essays — CS183C: Technology-Enabled Blitzscaling — Stanford University^[913]

Introduction: This column is a note to a Stanford student’s CS183c course, which was taught by Reid Hoffman and other Internet bosses. In each course, a person in charge of a giant company was invited to do an interview and explain how the company was scaled. The latest two featured Yahoo’s Jennifer Mae and Airbnb founder Brian Chesky. .

Natural Language Understanding with Distributed Representation^[914]

Introduction: Natural Language Understanding based on Distributed Representation (100+ pages), paper [915].

Recommender Systems Handbook^[916]

Introduction: Recommendation system manual.

Understanding LSTM Networks^[917]

Introduction: Understanding LSTM Network translation [918].

Machine Learning at Quora^[919]

Introduction: Machine learning in Quora.

On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models”^[920]

Introduction: Thinking Learning — RL+RNN Algorithm Information Theory.

The 5 Ways Data Scientists Keep Learning After College^[921]

Introduction: 5 ways data scientists can Continue learning after graduation.

Deep Learning in Neural Networks: An Overview^[922]

Deep learning in Neural networks.

The Contextual Learning.”^[923]

Introduction: Context learning, code [924].

Machine Learning For Complete Beginners^[925]

Introduction: Introduction to Machine learning, code [926].

2015 Excellent Doctoral Dissertation of China Computer Society (CCF)^[927]

Introduction :2015 CCF Excellent Doctoral Dissertation Award List.

“Learning to Hash Paper, Code and Dataset”^[928]

Learning to Hash Paper, Code and Dataset

Neural Networks with Theano and Lasagne^[929]

Introduction :(PyData2015) CNN/RNN tutorial based on Theano/Lasagne,github[930].

Handouts on Neural Networks and Deep Learning^[931]

Introduction: Lecture on Neural Network and Deep Learning prepared by Fudan University teacher Qiu Xipeng [932], PPT [933].

Microsoft Open Sources Distributed Machine Learning Toolkit^[934]

Microsoft Research Asia Open Source Distributed machine learning toolkit.

What is the technology behind Speech Recognition?^[935]

Introduction: Brief analysis of the technical principle of speech recognition

Michael I. Jordan^[936]

Michael I. Jordan’s home page. Many resources can be found according to the home page. Michael I. Jordan is a computer science and statistics scholar who specializes in machine learning and artificial intelligence. His important contributions include pointing out the link between machine learning and statistics and promoting widespread recognition in the machine learning community of the importance of bayesian networks.

“Geoff Hinton”^[937]

Geoffrey Everest Hinton FRS is an English born computer scientist and psychologist, best known for his work on neural networks. Hinton is one of the inventors of backpropagation and comparative divergence algorithms and an active promoter of deep learning. Through his homepage, you can find many papers and papers of excellent students. In addition, I recommend his student Yann Lecun[938] homepage

The Yoshua Bengio”^[939]

Yoshua Bengio is an expert in machine learning. If you don’t know, you can read the dialogue between Yoshua Bengio (top) and Yoshua Bengio (bottom).

Large Scale Deep Learning Within Google^[942]

Introduction: Google’s large-scale deep learning application evolution

Deep Learning: An MIT Press Book in Preparation^[943]

Introduction: Deep Learning ebook published by MIT, open ebook

A Mathematical Theory of Deep Convolutional Neural Networks for Feature Extraction^[944]

Introduction: Mathematical theory of feature extraction by deep convolutional Neural Network (CNN)

Microsoft Research Asia: Kaiming He^[945]

Introduction: Recommended Microsoft Asia Research he Kaiming home page

Speech and Language Processing (3rd Ed. Draft)^[946]

Introduction: Speech and Language Processing, third edition (draft)

LSA 311: Computational Semantics – Summer 2015^[947]

Introduction :Stanford’s new course “Computational Lexical Semantics”

Introduction to Statistical Machine Learning and Machine Learning by Prof. Zhang Zhihua from Shanghai Jiaotong University^[948]

Introduction: Statistical Machine Learning and Introduction to Machine Learning video by Zhang Zhihua, Professor of Shanghai Jiaotong University Probability basis [950]

Computational Linguistics and Deep Learning^[951]

Intro: Computational Linguistics and Deep Learning video [952] deep Learning: An Introduction from the NLP Perspective[953]

Black Hat USA 2015 – Deep Learning On Disassembly^[954]

Introduction :(BlackHat2015) traffic identification for deep learning applications (protocol identification/exception detection),[slide] (www.blackhat.com/docs/us-15/… [955])

LibRec: A Java Library for Recommender Systems^[956]

Introduction: a Java library for recommending systems

Multi-centrality Graph Spectral Decompositions and Their Application to Cyber Intrusion Detection^[957]

Introduction: Spectral Decomposition of Polycentric Graph and its Application in Network Intrusion Detection (Mc-gpca&mc-gdl)

Computational Statistics in Python^[958]

Use Python to calculate statistics

New Open Source Machine Learning Framework Written in Java^[959]

Datumbox – Framework — Java’s open source machine learning framework focuses on providing a large number of machine learning algorithms and statistical tests, and is able to handle small to medium-sized data sets

Awesome Recurrent Neural Networks^[960]

Introduction: Recursive neural network awesome series, covering books, projects, papers and so on

“Pedro Domingos”^[961]

Pedro Domingos is a professor at the University of Washington whose research interests are machine learning and data mining. At the ACM Webinar conference in 2015, a keynote presentation was presented on the five major genres of machine learning [962]. His personal page has a lot of papers on related research and courses he teaches.

Video Resources for Machine Learning^[963]

Introduction: Machine learning video collection

Deep Machine Learning Libraries and Frameworks

Deep machine learning libraries and frameworks

Big Data/Data Mining/Recommendation Systems/Machine Learning Related Resources^[964]

Introduction: the recommendation system resources in this article are very rich, the author is very intentional, excerpted the “recommendation system actual combat” cited in the paper.

Bayesian Methods in Astronomy: Hands-on Statistics^[965]

Introduction :(astronomy) bayes method /MCMC tutorial — statistical practice

Statistical Learning with Sparsity: The Lasso and Generalizations^[966]

Free book: Statistical Sparse Learning by Trevor Hastie[967] and Rob Tibshirani[968], both professors at Stanford University,Trevor Hastie has made a lot of contributions to the study of statistics

The Evolution of Distributed Programming in R^[969]

Introduction: Evolution of R distributed Computing, in addition to recommendations (R) Climate Change Visualization [970],(R) Introduction to Markov Chains [971]

Neon Workshop at Startup.ML: Sentiment Analysis and Deep Reinforcement Learning^[972]

Nervana Systems’ Presentation at Startup.ML[973] : Emotion Analysis and Deep reinforcement Learning

Understanding Convolution in Deep Learning^[974]

Introduction: Deep learning convolution concept details.

Python Libraries for Building Recommender Systems^[975]

Introduction :Python recommended system development library summary.

Neural Networks Class – Universite de Sherbrooke^[976]

An excellent introduction to deep learning by Hugo Larochelle (PhD student at Yoshua Bengio and former post-doc with Geoffrey Hinton).

Convolutional Neural Networks for Visual Recognition^[977]

Fy-fei Li & Andrej Karpathy, Slides + Video [978], Homework [979].

NIPS 2015 Deep Learning Symposium Part I^[980]

Introduction :NIPS 2015 Conference Summary Part I, Part II [981].

Python Machine Learning Introduction^[982]

Introduction to Python Machine learning

Reading Text in the Wild with Convolutional Neural Networks^[983]

Introduction: Reading Text in the Wild with Convolutional Neural Networks,Jaderberg. This journal article is a fusion of two previous meetings (ECCV14,NIPS14ws), locating and identifying text in pictures (called text spotting). End-to-end system: Detect Region and identify CNN. Papers, data, and code.

Yet Another Computer Vision Index To Datasets (YACVID)^[984]

Computer vision is a large data set index, containing 387 tags, a total of 314 data sets, click on the label cloud can find their own needs of the library.

Why SLAM Matters, The Future of Real-time SLAM, and Deep Learning vs SLAM^[985]

Introduction :Tombone’s summary of ICCV SLAM Workshop: The future of SLAM, SLAM vs Deep Learning focuses on monoSLAM and LSD-SLAM, and discusses the length of feature-based and feature-free methods. When the whole nation deep learning does visual perception, then read the geometry in CV.

Python Based Deep Learning Framework by Nervana™^[986]

Nervana Systems’ open source deep learning framework, Neon, was released.

MageNet and MS COCO Visual Recognition Challenges Video and Slider^[987]

Presentation: slides and videos from the ICCV 2015 ImageNet contest and the MS COCO Contest joint workshop.

An Introduction to Machine Learning with Python^[988]

Introduction to Python Machine Learning.

Neural Enquirer: Learning to Query Tables with Natural Language^[989]

Neural Enquirer 2nd edition

Deep Learning – Taking Machine Learning to the Next Level^[990]

[Google] Tensorflow-based deep learning/machine learning course

100 “Must Read” R-Bloggers’ Posts for 2015^[991]

R-bloggers 100 “Must Read” articles for 2015, the Gospel of R language Learning.

Machine Learning: A Probabilistic Perspective^[992]

“Machine Learning: A Probabilistic Perspective.” Undirected Graphical Models Markov Random Fields [993].

Deep Learning Book^[994]

This is an online deep learning book co-authored by Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Yoshua Bengio: How Can One Get Started with Machine Learning? [995]

UFLDL Recommended Readings^[996]

UFLDL’s recommended deep learning reading list.

CSE 705: Deep Learning (Spring 2015)^[997]

Suny At Buffalo Spring 2015 Machine Learning Course home page

Theano is a Deep Learning Python Library^[998]

Theano is one of the most popular Deep learning Python libraries, which also supports Gpus. Recommend Theano tutorial [999], the Document [1000]

Statistical Language Models Based On Neural Networks^[1001]

Introduction: doctoral dissertation: Neural Network Statistical Language Models.

Automatic Text Data Classification based on Machine Learning (PART 1)^[1002]

Introduction: Machine learning automatic classification method for text data (Part 2)[1003].

Pixel Recurrent Neural Networks^[1004]

Introduction: Using RNN to predict pixels, you can complement the blocked picture.

Computational Network Toolkit (CNTK)^[1005]

Microsoft Research put its deep learning toolkit CNTK, want to further understand and learn CNTK students can see a few days ago published “CNTK White paper” An Introduction to Computational Networks and the Computational Network Toolkit[1006].

Kalman and Bayesian Filters in Python^[1007]

Kalman filter, extended Kalman filter, untraced Kalman filter, etc., including exercises and reference answers

Statistical Inference for Data Science^[1008]

Introduction: Online free book: Statistical Inference for Data Science, R sample code, very good GitHub[1009]

Learning Deep Architectures for AI^[1010]

Introduction: this book is a tutorial written by Yoshua Bengio, which contains learning resources for learning the deep learning architecture used by artificial intelligence. The projects in the book have stopped updating the DeepLearnToolbox[1011].

Machine Learning Tutorials^[1012]

Introduction: This is a list of machine learning and deep learning tutorials, articles and resources. This list is written around various topics, including many categories related to deep learning, computer vision, reinforcement learning, and various architectures.

Notebooks of Data Science Ipython^[1013]

This is IPython notebook curated by Donne Martin. Topics cover big data, Hadoop, SciKit-Learn and the Scientific Python stack, among many others. As for deep learning, frameworks such as TensorFlow, Theano, and Caffe are also covered, along with specific architectures and concepts.

Open Source Deep Learning Server^[1014]

DeepDetect is an open source deep learning service implemented in C++ based on an external machine learning/deep learning library (currently Caffe). Examples of image training (ILSVRC) and text training (Word-based Sentiment Analysis, NIPS15) are given, as well as github indexing to ElasticSearch by image tags [1015].

Data Mining, Analytics, Big Data, and Data Science^[1016]

Data mining, data analysis and data science are the most popular data mining channel in the world. There are occasional machine learning picks.

Data Mining And Statistics: What’s The Connection?^[1017]

Introduction: Classic papers: Data Mining and Statistics.

Yoshua Bengio (NIPS ‘2015 Tutorial) Deep Learning^[1018]

NIPS ‘2015 Tutorial by Yoshua Bengio

NENO:Python Based Deep Learning Framework^[1019]

Nervana Systems’ open source deep learning framework, Neon, was released.

Matt Might:Reading for Graduate Students^[1020]

Introduction: A reading list for graduate students recommended by Professor Matt Might of the University of Utah.

Awesome Public Datasets^[1021]

Introduction: Open Data sets.

Introduction to Probability – The Science of Uncertainty^[1022]

Introduction :(edX) the science of uncertainty — introduction to probability theory (MITx).

R Software and Tools for Everyday Use^[1023]

Introduction: Recommended software and tools for R language development.

Implementing Dynamic Memory Networks^[1024]

Dynamic memory network implementation.

Deeplearning4j Chinese Homepage^[1025]

Introduction: English Home Page [1026]

Big Data Analysis Learning Resources: 50 Courses, Blogs, Tutorials, And More For Mastering Big Data Analytics^[1027]

50 Best Learning Resources for Big Data Analysis (Courses, Blogs, Tutorials, etc.)

A Full Hardware Guide to Deep Learning^[1028]

Introduction: A Comprehensive Hardware Guide to Deep Learning, from Gpus to RAM, CPU, SSD, PCIe, 原文[1029]

Deep Residual Networks^[1030]

Kaiming Open source work

The Definitive Guide to Natural Language Processing^[1031]

Introduction: The definitive guide to Natural Language Processing (NLP)

Evaluating Language Identification Performance^[1032]

How to do language detection on social media? What if there’s no data? Twitter has released a rare data set: 120,000 tagged Tweets in 70 languages

ICLR 2016 Accepted Papers

Introduction: ICLR 2016 is an important conference on Deep Learning and Machine Learning

Machine Learning: An In-depth, Non-Technical Guide – Part 1^[1033]

Introduction: Machine Learning — A Deep Non-technical Guide

Data Storytelling 101: Helpful Tools for Gathering Ideas, Designing Content & More^[1034]

Introduction: An introduction to Data Storytelling — Resource recommendations related to creative generation/data collection/content design

WikiTableQuestions: A Complex Real-world Question Understanding Dataset^[1035]

Introduction :WikiTableQuestions — complex Real question and answer data set

Big Data: 35 Brilliant And Free Data Sources For 2016

Introduction :(2016 version)35 great free large data sources

SPARKNET: Training Deep Networks in Spark^[1036]

Ion Stoica and Michael I. Jordan publish their first article together. CAFFE and SPARK are the perfect combination of distributed deep learning. github[1037]

The DeepLearning. University – An Annotated Deep Learning Bibliography | Memkite”^[1038]

Introduction: Deep learning (classification) bibliography

Learning Deep Learning^[1039]

Introduction: Deep learning reading lists

Awesome42 The Easiest Way to Find R Packages^[1040]

Description: Explore R pack’s great site Awesome 42

MLbase:Distributed Machine Learning Made Easy^[1041]

Introduction :MLbase is a research project of Prof. Dr. Tim Kraska[1042]. MLbase is a distributed machine learning management system

Deep Learning At Scale and At Ease^[1043]

Introduction: Introduction to the distributed deep learning platform SINGA[1044]

Learn All About Apache Spark (100x Faster than Hadoop MapReduce)^[1045]

Introduction :Spark Video Highlights

R For Deep Learning (I): Build Fully Connected Neural Network From Scratch^[1046]

R language deep Learning section 1: Starting from Scratch

A Visual Introduction to Machine Learning^[1047]

Introduction: Illustrating machine learning

Citation Network Dataset^[1048]

V7:2,244,021 Papers and 4,354,534 citation Relationships

Best Free Machine Learning Ebooks^[1049]

Introduction :10 best Free Books on Machine Learning

International Conference on Computer Vision (ICCV) 2015, Santiago^[1050]

Introduction :ICCV15 video collection

CaffeOnSpark Open Sourced for Distributed Deep Learning on Big Data Clusters^[1051]

Description :(Yahoo) Based on Hadoop/Spark distributed Caffe Caffe implementation

A Short Introduction to Learning to Rank^[1052]

Learning to Rank

Global Deep Learning Researcher^[1053]

Introduction: List of global deep learning experts, including researchers home page

Top Spark Ecosystem Projects^[1054]

Introduction :Spark Ecosystem Top Projects Summary [1055]

Proceedings of the 21st International Conference on Intelligent User Interfaces^[1056]

Proceedings of ACM IUI’16[1057] Conference Navigator-Proceedings [1058]

Machine Learning: An In-depth, Non-Technical Guide – Part 1^[1059]

Introduction: Deep Machine Learning,2[1060],3[1061],4[1062]

Oxford Deep Learning^[1063]

Nando de Freitas[1064] deep Learning course in Oxford, YouTube address [1065],Google DeepMind research Scientist, ComputervisionTalks [1066] also has an extensive cover. If you are doing machine vision research, I recommend reading other content as well. Must have been a big gain. Also, the YouTube page [1067] is loaded with videos

Neural Networks for Machine Learning^[1068]

Geoffrey Hinton’s MOOC on Coursera

Deep Learning News^[1069]

Hacker News for Deep learning. Keep up with news, research, and startups about deep learning. My friends in the machine learning, deep learning field suggest checking it out every day

The Maxout Networks”^[1070]

Introduction :Maxout Network Profiling

Advances in Neural Information Processing Systems^[1071]

Introduction: Paper collection of NIPS conferences

Machine Learning Applications in Genetics and Genomics^[1072]

Introduction: The application of machine learning in the field of bioengineering. If you are in the field of bioengineering, you can read a detailed article [1073]

Deep Learning in Bioinformatics^[1074]

Introduction: Deep learning applications in bioinformatics

A Few Useful Things to Know About Machine Learning^[1075]

Introduction: Some need to know about machine learning knowledge, for the students who have just entered the machine learning should read

Cambridge Machine Learning Group^[1076]

University of Cambridge machine Learning User Group home page, including some of the university of Cambridge machine learning experts and news

Randy Olson’s Data Analysis and Machine Learning Projects^[1077]

Introduction :Randy Olson’s[1078] library of data analysis and machine learning projects is good material for learning practices

GoLearn:Golang Machine Learning Library^[1079]

Golang Machine learning library is simple and easy to expand

“The Swift Ai”^[1080]

Introduction: Swift is used by many apple apps, but less for machine learning. Swift Ai does a lot of aggregation in this area. You can see

Please explain Support Vector Machines (SVM) Like I am a 5 year old^[1081]

Introduction: How to explain support Vector Machines (SVM) to a five-year-old

Reddit Machine Learning^[1082]

Introduction: Machine learning on Reddit

The ComputerVision resource”^[1083]

Links to some of the best blogs in the field of computer vision, some of the most powerful research institutions, etc. Do computer vision direction friends suggest paying more attention to the resources inside

Multimedia Laboratory Homepage^[1084]

Introduction: Deep Learning research homepage of The Chinese University of Hong Kong. In addition, the research team sorted out the latest progress of deep learning in 2013 and related papers [1085], among which the content of Useful Links benefited a lot

Search Engines that Learn from Their Users^[1086]

Introduction: This is a doctoral dissertation on search engines, which analyzes the widely used search engines such as Google and Bing. It is of great technical reference value to do search products

Deep Learning Books^[1087]

Introduction: Deep learning books are recommended (there are fewer of them, after all).

Deep Learning Books^[1088]

Introduction: Deep learning books are recommended (there are fewer of them, after all).

Towards Bayesian Deep Learning: A Survey^[1089]

Introduction: Bayes theorem in deep learning research papers.

Revisiting Distributed Synchronous SGD^[1090]

Introduction: Revisiting distributed gradient descent from Google Brain. Meanwhile, large-scale distributed deep network is recommended [1091]

Research Issues in Social Computing^[1092]

Introduction: Overview of social computing research issues.

What Are some Important Areas of Research in Social Computing right Now?^[1093]

Introduction: An overview of social computing applications, including some of the classic papers recommended

Collaborative Filtering Recommender Systems^[1094]

Introduction: Application of collaborative filtering in recommendation system.

Content-1951 Collaborative Filtering for Improved Recommendations^[1095]

Introduction: Research on collaborative filtering in content recommendation.

“Despite User-based and Item-based Collaborative Filtering Approaches by Similarity Fusion”^[1096]

Introduction: Collaborative filtering classical papers.

Item-based Collaborative Filtering Recommendation Algorithms^[1097]

Introduction: Collaborative filtering algorithm.

Amazon.com Recommendations Item-to-Item Collaborative Filtering^[1098]

Introduction: Amazon for collaborative filtering algorithm application.

Collaborative Filtering for Implicit Feedback Datasets^[1099]

Collaborative filtering for implicit feedback data set processing.

Tutorials, Papers and Code for Computer Graphics, Fractals and Demoscene^[1100]

Introduction: Computer graphics, geometry papers, tutorials, code. Make a recommended collection of computer graphics.

ELEN 6886 Sparse Representation and High Dimensional Geometry^[1101]

The Young Researcher Award, established in 2012 by Elsevier and now by PAMI(still sponsored by Elsevier), is awarded to those who have made outstanding contributions within seven years of completion of their PhD. Nominated by the CV community and announced at the CVPR meeting. The 2015 winner is John Wright, assistant professor at Columbia University, whose 2009 book sparse Representation for Robust Face Recognition [1102] has over 5K citations.

Software Engineer How to Learning Machine Learning^[1103]

Alex Smola, a renowned professor in the Department of Machine Learning at CMU, suggested on Quora how Programmers learn Machine Learning: Alex recommended many classic textbooks and materials in the fields of linear algebra, optimization, systems, and statistics.

Book Review: Fundamentals of Deep Learning^[1104]

Introduction: Book recommendation, fundamentals of deep learning. The source code [1105]

Learning from Big Code^[1106]

Introduction: The software engineering community is also interested in machine learning and natural language processing. Some people introduced the concept of “big code”, shared many code sets, and felt that ML could be used to predict code bugs, predict software behavior, and automatically write new code. Large code data set download

The Object Detection,^[1107]

Introduction: List of resources for target recognition by deep learning: Including RNN, MultiBox, SPP-NET, DEEPID-NET, Fast R-CNN, DeepBox, MR-CNN, Faster R-CNN, YOLO, DenseBox, SSD, Inside-Outside Net, G-CNN

Deep Learning: Course by Yann LeCun at College de France 2016(Slides in English)^[1108]

Yann LeCun 2016 Deep Learning Course by Yann LeCun at College de France 2016

Stanford HCI Group^[1111]

Introduction: Stanford Human-computer Interaction Group five CHI16 articles. 1. Behavioral economics research on crowdsourcing incentive mechanism: Batch settlement has a higher completion rate than single task. 2. Build connections between crowdsourcing experts and novices: microinternships. 3. Word embedding combined with crowdsourced verification of lexical subject classification (e.g. cats and dogs belong to pets). 4. Word embedding combined with target recognition activity prediction. 5. Encourage mistakes to speed up crowdsourcing.

Learn Data Science^[1112]

Introduction: Teach yourself data science

CS224D Lecture 7-Introduction to TensorFlow^[1113]

Abstract: this lesson is about TensorFlow, which is easy to take with a DeepDreaming model [1116].

Leaf-machine Learning for Hackers^[1117]

Introduction: Leaf is an open source framework for machine learning, designed for hackers, not scientists. It was developed with Rust, traditional machine learning, and today’s deep learning. Leaf[1118]

MXnet:Flexible and Efficient Library for Deep Learning^[1119]

Tutorial for NVidia GTC 2016 tutorial for NVidia GTC 2016[1121]

OpenAI Gym: Toolkit for Developing, Comparing Reinforcement Learning Algorithms^[1122]

OpenAI Gym: Development and comparison of reinforcement learning algorithm toolbox

Conference-iclr-2016 Papers and Code^[1123]

Introduction: Machine learning conference ICLR 2016 paper collection of code

Probabilistic Graphical Models Principles and Techniques^[1124]

Introduction: This book is written by Daphne Koller, the great leader of probabilistic graph model at Stanford University. It mainly deals with the learning and inference problems of Bayesian network and Markov logical network, and has profound theoretical interpretation of PGM. It is a must-read book for learning probabilistic graph model. Medium difficulty, suitable for some ML based graduate students. Backup address [1125]

Information Theory, Inference, and Learning Algorithms^[1126]

Introduction: This book is written by David MacKay, a renowned expert in information theory at Cambridge University. It is different from many books on machine learning from the perspective of inference and MCMC. Suitable for graduate and undergraduate students.

Convex Optimization — Boyd and Vandenberghe^[1127]

Introduction: A very good book on Convex Optimization, which covers various convex-optimization methods and unconstrained Optimization, and introduces the basic concepts and theories of convex-optimization. Difficulty, suitable for optimization and machine learning have a certain basis of the crowd

Machine – Learning – Tom Mitchell^[1128]

Introduction: This book is a machine learning textbook written by Mitchell, director of CMU Machine learning Department. It’s not very difficult. Suitable for beginners, undergraduate and graduate students

Learning with Kernels Support Vector Machines, Regularization, Optimization, and Beyond^[1129]

Learning with Kernels PPT[1130] Learning with Kernels PPT[1131]

The Elements of Statistical Learning: Data Mining, Inference, and Prediction^[1132]

Introduction: Stanford statistics department three god statistics learning textbooks, partial statistics and learning theory, need to have a certain foundation of linear algebra, statistics and probability theory, high difficulty, suitable for graduate students

Data Mining: Practical Machine Learning Tools and Techniques^[1133]

Introduction: This book is an application machine learning guide written by the author of the famous machine learning tool Weka. It is very practical and easy to use. It is suitable for liberal arts and various applied sciences

Foundations of Statistical Natural Language Processing^[1134]

Introduction: This book is also a popular NLP textbook. It mainly covers statistical NLP methods. It is written by Chirs Manning, another big name at Stanford

Speech and Language Processing^[1135]

Introduction: The most commonly used NLP teaching material in North America, the introduction to Natural language processing course written by Stanford Jurafsky, has a relatively comprehensive coverage and low difficulty. Suitable for undergraduate and graduate students

Natural Language Processing with Python-NLTK^[1136]

Introduction: Practical tutorial, the famous tool NLTK author’s book, suitable for undergraduates and beginners to learn by hand

“The NLP compromise”^[1137]

Natural Language Processing Learning Kit to help you understand what is natural language processing

Multi-way, Multilingual Neural Machine Translation with a Shared Attention Mechanism^[1138]

A multichannel/multilingual perceptual machine translation model based on attention-sharing mechanism

Machine learning, deep learning materials 1000+

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