Basics of Machine Learning

• Here is a “short” list of the most popular and up-to-date resources for learning machine learning and AI related topics.
• “You should be practicing!”

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Basic Courses

• Stanford CS229 - Machine Learning
  • By Andrew Ng. This course provides a broad introduction to machine learning and statistical pattern recognition. Videos and lectures are available freely.
  • 斯坦福机器学习CS229课程讲义的中文翻译
• Cornell CS4780/CS5780 - Machine Learning for Intelligent Systems
  • Lectures and video recordings are available for free.
• Dive into Deep Learning: an interactive deep learning book with code, math, and discussions
  • The Github repo for the book is here
  • The version based on numpy

Handy Cheatsheets

• VIP cheatsheets for Stanford’s CS 229 Machine Learning
  • This one has a Chinese version
• Essential Cheat Sheets for deep learning and machine learning researchers

Curated and Awesome List of Resources

• Awesome Machine Learning
  • A curated list of awesome machine learning frameworks, libraries and software (by language).
• Awesome Deep Learning
  • A curated list of awesome Deep Learning tutorials, projects and communities
• Awesome Tensorflow
  • A curated list of dedicated resources

Papers and Algorithms

• The GAN Zoo - A list of all named GANs
• generative-models - Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow
• tensorflow-recipes - A collection of TensorFlow (Tensorpack) implementations of recent deep learning approaches including pretrained models

Materials In Chinese

• PumpkinBook - 《机器学习》（西瓜书）公式推导解析
  • In Chinese. Read online here
• 动手学深度学习 - 面向中文读者的能运行、可讨论的深度学习教科书
• 斯坦福大学2014（吴恩达）机器学习教程中文笔记
• DeepLearning-500-questions - 深度学习500问
  • Deep learning through 500 questions, a Markdown book.

Tutorial and Examples

• Machine Learning From Scratch
  • Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning
• Homemade Machine Learning
  • Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained
• Deep Learning Models
  • A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks

More Serious Stuff

• Deep Learning Papers Reading Roadmap