Writing in the front

Compiled CV this way some of the dry goods, I have studied, have I haven’t studied, intends to back met good resources, or data update, convenient to hold yourself back, also can give others learn, I set out to the data connection, the mathematics that I put the PDF in the cloud disk inside, More details can be found on my GitHub.

I just want to share technical blog posts, but also want to say that if you think it is useful, please click on the following, give a like, also a relief to me, after all, I have to lose a lot of hair, hey hey hey

Mathematics – Advanced mathematics

  • Mathematics Foundation: Advanced mathematics
  • Set of advanced mathematical knowledge
  • Advanced Mathematics 7th Edition (Volume 1)
  • Advanced Mathematics 7th Edition (Volume 2)

Mathematics – Linear algebra

  • Math Basics: Linear algebra
  • Linear algebra courseware (complete version) Tongji University
  • Introduction to Matrix Analysis Luo Jiahong (4th Edition)

Mathematics – Probability theory

  • Fundamentals of mathematics: Probability theory and mathematical statistics
  • Probability theory and mathematical statistics formula collation (ultra full free edition)PDF
  • Lectures on probability and mathematical statistics
  • Probability theory and Mathematical Statistics Courseware (PPT)

Mathematics – convex optimization

  • Convex optimization. Boyd_ Translated by Wang Shuning

  • ConvexOptimization_Boyd_slides

  • Convex Optimization_Solutions

  • Convex optimization knowledge point arrangement

    • The introduction of Ch1-2
    • Ch3 convex function
    • Ch4 convex optimization problem
    • Ch5 duality problem
    • Ch6 approximation and fitting
    • Ch7 statistical estimation
    • Ch8 geometry problem
    • Ch9 unconstrained optimization
    • Ch10 equation constraint optimization
    • Ch11 interior-point method
    • Convex optimization focus collation & summary

Fundamentals of machine learning — Mathematical transitions

  • Statistical learning method
  • Zhou zhihua – Machine learning (watermelon book) Formula derivation in the book
  • Stanford University Fundamentals of Machine Learning in Mathematics

Deep learning

  • Introduction to Deep learning 300 PPT
  • Neural networks and deep learning
  • Neural Networks and Deep Learning — Qiu Xipeng, Fudan University
  • Deep Learning — Flower book
  • Deep Learning 500 Questions

Computer vision

  • Computer Vision: Models, Learning and Inference
  • Computer Vision: Algorithms and Applications
  • Introduction to OpenCV3 Programming

Theory of actual combat

  • Scikit-learn and TensorFlow Machine Learning Practical Guide

Online courses

Machine learning

  • Ng notes in Chinese on Machine Learning homepage

This is definitely the number one course for getting started with machine learning. Even if you don’t have a solid foundation in probability theory, linear algebra and other mathematics required for machine learning, you can easily start this introductory machine learning course and experience the endless fun of machine learning. The course was delivered by netease Cloud Classroom, with Chinese subtitles translated by Huang Haiguang and others. This course has Chinese notes and assignment codes.

  • Ng CS229 Home page Chinese notes

Ng’s Stanford Machine Learning course, CS229, is similar to Ng’s Coursera Machine Learning, but slightly more difficult with more mathematical requirements and derivation of formulas. This course provides an extensive introduction to machine learning and statistical pattern recognition.

  • Lin Xuantian, “Fundamentals of Machine Learning” Chinese course Chinese notes matching books

The course fundamentals of Machine Learning taught by Lin Xuantian from Taiwan University is comprehensive and covers many aspects in the field of machine learning. It is very suitable for the introduction and advanced data of machine learning. And Lin’s teaching style is also very humorous, always let readers in a relaxed and happy atmosphere to master the knowledge. This course is slightly more difficult than Ng’s Machine Learning and focuses on Machine Learning theory.

  • Lin Xuantian, Machine Learning Techniques Chinese course Notes

Machine Learning Techniques is an advanced course of Fundamentals of Machine Learning. This paper mainly introduces some classical algorithms of machine learning, including support vector machine, decision tree, random forest, neural network and so on. It is slightly harder than Fundamentals of Machine Learning and very practical.

Deep learning

  • Course notes for Deep Learning Chinese course

After The machine Learning course opened by Ng, the course “Deep Learning” released by Ng was also well received. The biggest feature of Ng’s course is that it teaches you knowledge step by step, which is a rare good video for beginners. The whole project includes five courses: 01. Neural networks and deep learning; 02. Improved deep neural networks – hyperparametric debugging, regularization and optimization; 03. Structured machine learning projects; Convolutional Neural network; 05. Sequence model.

  • Ai programmer deep learning combat video address Chinese video English notes original Chinese video

Speaking of open courses on Deep Learning, on par with Ng’s Deep Learning, another open course is Programmer Deep Learning By Fast. Ai. The biggest characteristic of this course is “top-down” rather than “bottom-up”, which is an excellent in-depth learning course through actual practice.

Reinforcement learning

  • Reinforcement Learning-David Silver Chinese Course

In the same way that Ng’s course is for beginners of machine learning and deep learning, David Silver’s course is definitely a must for most people learning reinforcement. The course covers intensive learning in detail from shallow to deep. However, due to the difficulty of reinforcement learning itself, there is still a certain threshold to listen to this course. It is suggested that watching this video after having a general understanding of this field will have a better learning effect and make it easier to find the key points of learning.

  • Li Hongyi “Deep Reinforcement Learning” Chinese course Chinese notes course PPT

Although David Silver’s course is detailed, many cutting-edge topics are not included in it. At this time, Deep Reinforcement Learning by Li Hongyi of National Taiwan University is the best choice to learn cutting-edge trends.

Computer vision

  • Stanford CS223B

It’s for the basics, it’s for those of you who are just getting started, it’s a little bit less about deep learning, it’s not going to be a whole course about deep learning, it’s going to be about computer vision, it’s going to be about everything

  • Professor Wei Ying’s lecture notes on image processing in Northeastern University

    • Digital image processing (1) Introduction
    • Digital image processing (2) Basic digital image processing
    • Digital image processing (3) image transformation
    • Digital image processing (4) digital enhancement
    • Digital image processing (5) image restoration
    • Digital image processing (6) image compression
    • Digital image processing (7) image segmentation
    • Northeastern University graduate course – Digital image processing – final materials summary
    • Introduction to R-CNN and Faster R-CNN for target detection
    • Image Semantic Segmentation Based on FNC and Pascal-VOC Data Set
    • Image segmentation of coins, pins, rice code details
    • Principle of maximum entropy image restoration Method
    • Principle and Code Practice of bilateral Filtering Method
    • Traffic light identification code analysis based on OpenCV

  • Netease cloud classroom “Opencv Computer Vision Actual Combat” study notes

    • Computer vision combat (a) open a visual combat column
    • Computer vision actual combat (2) image basic operation
    • Computer vision combat (3) threshold and smoothing processing
    • Computer vision actual combat (4) image morphology operation
    • Computer vision combat (5) image gradient calculation
    • Computer vision combat (6) edge detection
    • Computer vision practice (7) image pyramid and contour detection
    • Histogram and Fourier transform
    • Credit card digital recognition project (complete code attached)
    • Harris Corner Detection of image features (complete code attached)
    • Scale Invariant Feature Transform (SIFT)
    • Computer Vision practice (12) Panoramic image Mosaic (complete code attached)
    • Computer vision combat (13) Parking lot identification (with complete code)
    • Answer Sheet Identification (With complete code)
    • Computer vision Practice (15) Background modeling (with complete code)
    • Computer Vision Field (16) Optical flow estimation
    • OpenCV DNN model (with complete code)

The paper

Object Detection is a core research field and an important branch in CV field of deep learning. From 2013 to 2019, from the earliest R-CNN and Fast R-CNN to the later YOLO V2 and YOLO V3 and this year’s M2Det, new models emerge one after another and their performance is getting better and better!

  • Arxiv Stats Is the home page of Arxiv machine learning

Reference documents and tools

  • Kaggle homepage route

Competition is the most effective way to improve their machine learning ability, the first choice of Kaggle competition.

  • TensorFlow official document In Chinese
  • Scikit-learn Official document Official document In Chinese

Scikit-learn is a very comprehensive library for machine learning and is a rare practical programming manual.

  • PyTorch official documentation in Chinese
  • Mathpix “address

It takes a screenshot and the formula is automatically converted into LaTex expressions, which we simply need to modify.

  • Address of Markdown
  • Mdnice address
  • Zotero download

Zotero is widely used as free software to help researchers collect, manage, and reference research resources. This instruction mainly shares and quotes research resources, which can include journals, books and other literature, web pages and pictures.

project

The meeting

other

  • XMind address
  • MathPix address
  • TensorFlow’s online editor colab address