A list of books, courses, and examples on artificial intelligence, deep learning, and Tensorflow is part of my Awesome Links series. See the DataScienceAI Links Series for additional collections, models, open source tools and frameworks. This paper recommends some open source Books can be convenient to Awesome – CS – Books – Warehouse through, or go to AI CheatSheet, AIDL – Series | artificial intelligence and depth study of actual combat, AIDL Workbench | example, algorithms, Model, application for more details and code implementation.

Mathematics | mathematical basis

• 2008- statistics complete course #Book# : the complete course of statistics, written by l. wasserman, a famous American statistician, is an excellent textbook that contains almost all the knowledge in the field of statistics. This book not only introduces all the contents of traditional mathematical statistics, but also includes the Bootstrap method (self-help method), independent inference, causal inference, graph model, non-parametric regression, orthogonal function smoothing method, classification, statistical theory, data mining and other new methods and techniques in the field of statistics. This book not only focuses on the basic theory of probability and mathematical statistics, but also emphasizes the training of data analysis ability. This book contains a large number of examples to help readers quickly master statistical data analysis using R software.

• 2009-Convex Optimization #Book#:This book is about convex optimization, a special class of mathematical optimization problems, which includes least-squares and linear programming problems. It is well known that least-squares and linear programming problems have a fairly complete theory, arise in a variety of applications, and can be solved numerically very efficiently. The basic point of this book is that the same can be said for the larger class of convex optimization problems.

• 2009-The Elements of Statistical Learning: Data Mining, Inference, and Prediction-Second Edition: Hastie and Tibshirani cover a broad range of topics, from supervised learning (prediction) to unsupervised learning including neural networks, support vector machines, classification trees and boosting—the first comprehensive treatment of this topic in any book.

• 2010-All of Statistics: A Concise Course in Statistical Inference #Book#: The goal of this book is to provide a broad background in probability and statistics for students in statistics, Computer science (especially data mining and machine learning), mathematics, and related disciplines.

• # 2012 – expericnce – statistical methodology Book# : this book comprehensively systematically introduces the main methods of statistical learning, especially the supervision method of study, including the perception, k neighbor method, naive bayesian method, decision tree, logistic regression and the entropy model, support vector machine (SVM), promotion methods, the EM algorithm, the hidden markov model and conditional random field, etc.

• 2016-Immersive Linear Algebra #Book#: The World’s First Linear Algeria Book with fully Interactive Figures.

• 2017-The Mathematics of Machine Learning #Book#: Machine Learning theory is a field that intersects statistical, probabilistic, computer science and algorithmic aspects arising from learning iteratively from data and finding hidden insights which can be used to build intelligent applications. Despite the immense possibilities of Machine and Deep Learning, a thorough mathematical understanding of many of these techniques is necessary for a good grasp of the inner workings of the algorithms and getting good results.

The Machine Learning | Machine Learning

• 2007-Pattern Recognition And Machine Learning #Book#: The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics.

• 2012-Machine Learning A Probabilistic Perspective #Book#: This textbook offers a comprehensive and self-contained introduction to the field of machine learning, a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning.

• 2014-The Cambridge Handbook of Artificial Intelligence #Book#: With a focus on theory rather than technical and applied issues, the volume will be valuable not only to people working in AI, but also to those in other disciplines wanting an authoritative and up-to-date introduction to the field.

• 2015-Data Mining, The Textbook #Book#: This textbook explores the different aspects of data mining from the fundamentals to the complex data types and their applications, capturing the wide diversity of problem domains for data mining issues.

• 2016-Dive into Machine Learning #Book#: I learned Python by hacking first, and getting serious later. I wanted to do this with Machine Learning. If this is your style, join me in getting a bit ahead of yourself.

• As an introductory textbook in this field, machine learning covers all aspects of basic knowledge of machine learning as far as possible. Introduces the basic knowledge of machine learning, classical and commonly used machine learning method, decision tree, neural network, support vector machine (SVM), bayes classifier, integration, clustering, dimension reduction and measure learning), feature selection and sparse, computational learning theory, a semi-supervised learning, probability graph model, rules, and reinforcement learning, etc.

• 2016-Prateek Joshi-Python Real World Machine Learning #Book#: Learn to solve challenging data science problems by building powerful machine learning models using Python.

• 2016-Designing Machine Learning Systems with Python: Gain an understanding of the machine learning design process, Optimize machine learning systems for improved accuracy, Understand common programming tools and techniques for machine learning, Develop techniques and strategies for dealing with large amounts of data from a variety of sources, Build models to solve unique tasks.

• 2018-AndrewNG-Machine Learning Yearning #Book#: This book is focused not on teaching you ML algorithms, but on how to make ML algorithms work. Some technical AI classes will give you a hammer; this book teaches you how to use the hammer.

• 2018-Artificial Intelligence: A Modern Approach-3rd Edition #Book#:Artificial Intelligence: A Modern Approach, 3e offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence.

Reinforcement Learning | intensive study

• 2018-Reinforcement Learning: An Introduction-Second Edition #Book#: This textbook provides a clear and simple account of the key ideas and algorithms of reinforcement learning that is accessible to readers in all the related disciplines. Familiarity with elementary concepts of probability is required.

DeepLearning | deep learning

• 2015-Goodfellow, Bengio and Courville-The Deep Learning Textbook #Book# The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. The online version of the book is now complete and will remain available online for free.

• 2016-Stanford Deep Learning Tutorial #Book#: This tutorial will teach you the main ideas of Unsupervised Feature Learning and Deep Learning. By working through it, you will also get to implement several feature learning/deep learning algorithms, get to see them work for yourself, and learn how to apply/adapt these ideas to new problems.

• 2016-Building Machine Learning Projects with TensorFlow #Book#: Engaging projects that will teach you how complex data can be exploited to gain the most insight.

• 2016- introduction to deep learning #Book# : the book you’re reading is an “interactive” ebook — each chapter can run inside a Jupyter Notebook. We packed Jupyter, PaddlePaddle, and all the dependent software into a Docker image. So you don’t need to install all kinds of software yourself, just need to install Docker.

• 2017-Neural Networks and Deep Learning #Book#: Neural Networks and Deep Learning is a free online book. The book will teach you about: (1) Neural networks, a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data. (2) Deep learning, a powerful set of techniques for learning in neural networks

• 2017-TensorFlow Book #Book#: Accompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.

NLP | natural language processing

• 2016-Text Data Management and Analysis #Book#: A Practical Introduction to Information Retrieval and Text Mining

• 2017-DL4NLP-Deep Learning for NLP resources: State of the art resources for NLP sequence modeling tasks such as machine translation, image captioning, and dialog.

Computer Vision | Computer Vision

• 2016-OpenCV: Computer Vision Projects with Python: Use OpenCV’s Python bindings to capture video, manipulate images, and track objects. Learn about the different functions of OpenCV and their actual implementations.

DataScience | generic data science

• Simple data Analysis – Chinese Version In a lively form similar to “Zhang Hui novel”, Data Analysis vividly shows the skills that a good data analyst should know and know: Basic steps of data analysis, experimental method, optimization method, hypothesis testing method, Bayesian statistical method, subjective probability method, heuristic method, histogram method, regression method, error processing, relevant database, data processing skills; After the text, there are three appendices to introduce the ten most important aspects of data analysis, R tools and ToolPak tools, which not only fully show the target knowledge, but also build a bridge for readers to further research.

• 2014-DataScience From Scratch #Book#: In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch.

• 2016-Python Data Science Handbook #Book#:Jupyter Notebooks for the Python Data Science Handbook

DataScienceAI Course Links | machine learning, deep learning and natural language processing field recommended list of courses

The Machine Learning | Machine Learning

• 2010-MIT Artifical Intelligence Videos: This course includes interactive demonstrations which are intended to stimulate interest and to help students gain intuition about how artificial intelligence methods work under a variety of circumstances.

• 2014-Stanford – Machine Learning #Course#: In this course, you will learn the most efficient machine learning techniques, understand how to use them, and practice them yourself. More importantly, you will learn not only theoretical knowledge, but also how to practice and quickly use powerful techniques to solve new problems. Finally, you’ll learn how companies in Silicon Valley are innovating in machine learning and AI.

• 2014-Statistical Learning (Self-Paced) #Course#: This is an introductory-level course in supervised learning, with a focus on regression and classification methods.

• 2015-Udacity-Intro to Artificial Intelligence #Course#: In this course, you’ll learn the basics of modern AI as well as some of the representative applications of AI. Along the way, we also hope to excite you about the numerous applications and huge possibilities in the field of AI, which continues to expand human capability beyond our imagination.

• 2016- Course#: Linear Support Vector Machine (SVM) :: Course Introduction @Machine Learning Techniques, etc.

• 2017-EdX-Artificial Intelligence (AI) #Course#: Learn the fundamentals of Artificial Intelligence (AI), and apply them. Design intelligent agents to solve real-world problems including, search, games, machine learning, logic, and constraint satisfaction problems.

• 2018-Machine Learning Crash Course with TensorFlow APIs by Google #Course#: Machine Learning Crash Course features a series of lessons with video lectures, real-world case studies, and hands-on practice exercises.

Deep Learning

• 2016-Deep Learning by Google #Course#: In this course, you’ll develop a clear understanding of the motivation for deep learning, and design intelligent systems that learn from complex and/or large-scale datasets.

• 2017-CS 20SI: TensorFlow for Deep Learning Research #Course#: This course will cover the fundamentals and contemporary usage of the TensorFlow library for deep learning research. We aim to help students understand the graphical computational model of TensorFlow, explore the functions it has to offer, and learn how to build and structure models best suited for a deep learning project.

• 2017-Fast.ai DeepLearning AI #Course#: Most of the library is quite well tested since many students have used it to complete the Practical Deep Learning for Coders course. However it hasn’t been widely used yet outside of the course, so you may find some missing features or rough edges.

NLP | natural language processing

• 2016-University of Illinois at Urbana-Champaign:Text Mining and Analytics #Course#: This course will cover the major techniques for mining and analyzing text data to discover interesting patterns, extract useful knowledge, and support decision making, with an emphasis on statistical approaches that can be generally applied to arbitrary text data in any natural language with no or minimum human effort.

• 2017-Neural Networks for Machine Learning #Course#: Learn about artificial neural networks and how they’re being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc.

• 2017-Oxford Deep NLP course #Course#: This is an advanced course on natural language processing. Automatically processing natural language inputs and producing language outputs is a key component of Artificial General Intelligence.

• 2017-CS224d: Deep Learning for Natural Language Processing #Course#: Intro to NLP and Deep Learning, Simple Word Vector representations: word2vec, GloVe, etc.

Industrial Applications | industry application

Autonomous Driving | automatic Driving

• 2017-Artificial Intelligence for Robotics #Course#: Learn how to program all the major systems of a robotic car from the leader of Google and Stanford’s autonomous driving teams.

Examples of | demonstration

• 2015-Trained image classification models for Keras #Project#: Keras code and weights files for popular deep learning models.

• All-in-one Docker image for Deep Learning #Project#: An all-in-one Docker image for deep learning. Contains all the popular DL frameworks (TensorFlow, Theano, Torch, Caffe, etc.)

• Top Deep Learning Projects: A list of popular github projects related to deep learning (ranked by stars).

TensorFlow Learning & Practices the Links | TensorFlow data index

The Overview | Overview

• 2017- TensorFlow demystified: To understand a new framework, Google’s TensorFlow is a framework for machine-learning calculations, it is often useful to see a ‘toy’ example and learn from it.

• How to Use TensorFlow as a computing framework: If you are new to TensorFlow and want to use it as a computing framework, this article is a good choice and will help you read it.

• 2017-We Need to Go Deeper: A Practical Guide to TensorFlow and Inception

Case Study | Case analysis

• 2017-Top Five Use Cases of Tensorflow: TensorFlow is mainly used for: Classification, Perception, Understanding, Discovering, Prediction and Creation.

• 2018-Google Brain researchers explain chatbots: Deep learning technology issues and TensorFlow-based development practices.

• How Zendesk Serves TensorFlow Models in Production

The Resource | Resource collection

The Series | Series

• 2015-tensorflow_tutorials: From the basics to slightly more interesting applications of Tensorflow

• 2017-Effective TensorFlow: My attempt is to gradually expand this series by adding new articles and keep the content up to date with the latest releases of TensorFlow API.

• 2017-TensorFlow 101: TensorFlow is an open source machine learning library developed at Google. TensorFlow uses data flow graphs for numerical computations.

• 2017-TensorFlow-World: This repository is aimed to provide simple and ready-to-use tutorials for TensorFlow.

Examples | sample

• 2015-TensorFlow Examples: This tutorial was designed for easily diving into TensorFlow, through examples. For readability, it includes both notebooks and source codes with explanation.

• 2016-Deep Learning Using Tensorflow: This repository contains the code for Tensorflow Tutorials for Deep Learning from Starting to End. All the code is written using Python3.

• 2017-Deep Learning 21 Examples: This project is the supporting code of “21 projects to play deep learning — Based on TensorFlow practice in detail”, the code recommended running environment is: Ubuntu 14.04, Python 2.7, TensorFlow >= 1.4.0. Try to run the code in this book on a Unix-like system and Python 2.

• 2017-TensorFlow Models by Sarasra #Project#: This repository contains a number of different models implemented in TensorFlow: the official models, the research models, the samples folder and the tutorials folder.

• Android TensorFlow Machine Learning Example: This article is for those who are already familiar with machine learning and know how to the build model for machine learning(for this example I will be using a pre-trained model).

• 2018-Deep Learning Using Tensorflow: This repository contains the code for Tensorflow Tutorials for Deep Learning from Starting to End. All the code is written using Python3.

• 2018-TensorFlow Project Template #Project#: A simple and well designed structure is essential for any Deep Learning project, so after a lot of practice and contributing in tensorflow projects here’s a tensorflow project template that combines simplcity, best practice for folder structure and good OOP design.

• 2018-Beginner Tensorflowjs Examples in Javascript: This is the easiest set of Machine Learning examples that I can find or make. I hope you enjoy it.

Collection

• Awesome TensorFlow #Collection#: A curated list of awesome TensorFlow experiments, libraries, and projects. Inspired by awesome-machine-learning.

• TensorFlow-World-Resources #Collection#: Organized & Useful Resources about Deep Learning with TensorFlow

Tutorial | Tutorial

• 2016-Tensorflow in a Nutshell — Part One: Basics: The fast and easy guide to the most popular Deep Learning framework in the world.

• 2016-TensorFlow architecture: One of the characteristics of TF is that it can support a wide range of devices, from large Gpus and CPUS to small mobile phones and tablets. TF can run on all kinds of devices.

• 2016-Image Completion with Deep Learning in TensorFlow

• 2017-NakedTensor: Bare bone examples of machine learning in TensorFlow.

• 2017-Deep Learning in 7 lines of code: The essence of machine learning is recognizing patterns within data. This boils down to 3 things: data, software and math. What can be done in seven lines of code you ask? A lot.

• You need to understand Tensor and Flow at the same time. This paper first introduces the core concepts and basic overview of TensorFlow, and then analyzes the OpKernels module, Graph module, Session module.

• 2017-TensorFlow Introduction: Matrices, Multicharacteristic linearity, and Logistic Regression: This is an introduction to TensorFlow series written by Hin Khor, co-organizer of TensorFlow Gathering in Tokyo, Japan.

• 2017-We Need to Go Deeper: A Practical Guide to TensorFlow and Inception