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!”

--------------

Basic Courses
-------------

-  `Stanford CS229 - Machine
   Learning <https://see.stanford.edu/Course/CS229>`__

   -  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 <https://github.com/Kivy-CN/Stanford-CS-229-CN>`__

-  `Cornell CS4780/CS5780 - Machine Learning for Intelligent
   Systems <http://www.cs.cornell.edu/courses/cs4780/2018fa/>`__

   -  Lectures and video recordings are available for free.

-  `Dive into Deep Learning: an interactive deep learning book with
   code, math, and discussions <https://d2l.ai/>`__

   -  The `Github repo for the book is
      here <https://github.com/d2l-ai/d2l-en>`__
   -  `The version based on numpy <http://numpy.d2l.ai/>`__

Handy Cheatsheets
-----------------

-  `VIP cheatsheets for Stanford’s CS 229 Machine
   Learning <https://github.com/afshinea/stanford-cs-229-machine-learning>`__

   -  This one has a `Chinese
      version <https://github.com/afshinea/stanford-cs-229-machine-learning/tree/master/zh>`__

-  `Essential Cheat Sheets for deep learning and machine learning
   researchers <https://github.com/kailashahirwar/cheatsheets-ai>`__

Curated and Awesome List of Resources
-------------------------------------

-  `Awesome Machine
   Learning <https://github.com/josephmisiti/awesome-machine-learning>`__

   -  A curated list of awesome machine learning frameworks, libraries
      and software (by language).

-  `Awesome Deep
   Learning <https://github.com/ChristosChristofidis/awesome-deep-learning>`__

   -  A curated list of awesome Deep Learning tutorials, projects and
      communities

-  `Awesome Tensorflow <https://github.com/jtoy/awesome-tensorflow>`__

   -  A curated list of dedicated resources

Papers and Algorithms
~~~~~~~~~~~~~~~~~~~~~

-  `The GAN Zoo - A list of all named
   GANs <https://github.com/hindupuravinash/the-gan-zoo>`__

-  `generative-models - Collection of generative models, e.g. GAN, VAE
   in Pytorch and
   Tensorflow <https://github.com/wiseodd/generative-models>`__

-  `tensorflow-recipes - A collection of TensorFlow (Tensorpack)
   implementations of recent deep learning approaches including
   pretrained models <https://github.com/PatWie/tensorflow-recipes>`__

Materials In Chinese
--------------------

-  `PumpkinBook -
   《机器学习》（西瓜书）公式推导解析 <https://github.com/datawhalechina/pumpkin-book>`__

   -  In Chinese. Read online
      `here <https://datawhalechina.github.io/pumpkin-book/>`__

-  `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 <https://zh.d2l.ai/index.html>`__
-  `斯坦福大学2014（吴恩达）机器学习教程中文笔记 <https://github.com/fengdu78/Coursera-ML-AndrewNg-Notes>`__
-  `DeepLearning-500-questions -
   深度学习500问 <https://github.com/scutan90/DeepLearning-500-questions>`__

   -  Deep learning through 500 questions, a Markdown book.

Tutorial and Examples
---------------------

-  `Machine Learning From
   Scratch <https://github.com/eriklindernoren/ML-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 <https://github.com/trekhleb/homemade-machine-learning>`__

   -  Python examples of popular machine learning algorithms with
      interactive Jupyter demos and math being explained

-  `Deep Learning
   Models <https://github.com/rasbt/deeplearning-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 <https://github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap>`__