Collection of Useful Tools for Machine Learning and Artificial Intelligence
===========================================================================

Basic Tools
-----------

-  `autograd - Efficiently computes derivatives of numpy
   code <https://github.com/HIPS/autograd>`__

   -  Very useful tool when building statistical and machine learning
      tools in Python. **Autograd** can automatically differentiate
      native Python and Numpy code.

Machine Learning Framework
--------------------------

-  `tensorflow - An Open Source Machine Learning Framework for
   Everyone <https://github.com/tensorflow/tensorflow>`__

   -  TensorFlow is an open source software library for numerical
      computation using data flow graphs. The graph nodes represent
      mathematical operations, while the graph edges represent the
      multidimensional data arrays (tensors) that flow between them.
      This flexible architecture enables you to deploy computation to
      one or more CPUs or GPUs in a desktop, server, or mobile device
      without rewriting code.
   -  `The official tutorial of
      tensorflow <https://www.tensorflow.org/tutorials/>`__
   -  `Models and examples built with
      TensorFlow <https://github.com/tensorflow/models>`__
   -  `TensorFlow
      官方文档中文版 <https://github.com/jikexueyuanwiki/tensorflow-zh>`__
   -  `TensorFlow-Examples - TensorFlow Tutorial and Examples for
      Beginners (support TF v1 &
      v2) <https://github.com/aymericdamien/TensorFlow-Examples>`__
   -  `awesome-tensorflow - A curated list of dedicated
      resources <https://github.com/jtoy/awesome-tensorflow>`__

-  `keras - Deep Learning for
   humans <https://github.com/keras-team/keras>`__

   -  Keras is a high-level neural networks API, written in Python and
      capable of running on top of TensorFlow, CNTK, or Theano. It was
      developed with a focus on enabling fast experimentation.
   -  `Official documents of keras <https://keras.io/>`__
   -  `Keras code and weights files for popular deep learning models by
      the author <https://github.com/fchollet/deep-learning-models>`__
      (A little out of date.)
   -  **Keras** also lives in **tensorflow** now: “``tf.keras`` is
      TensorFlow’s implementation of the Keras API specification. This
      is a high-level API to build and train models that includes
      first-class support for TensorFlow-specific functionality”

-  `pytorch - Tensors and Dynamic neural networks in Python with strong
   GPU acceleration <https://github.com/pytorch/pytorch>`__

   -  Usually one uses PyTorch either as 1) a replacement for NumPy to
      use the power of GPUs; 2) a deep learning research platform that
      provides maximum flexibility and speed.

-  `scikit-learn - machine learning in
   Python <https://github.com/scikit-learn/scikit-learn>`__

   -  **scikit-learn** is a Python module for machine learning built on
      top of SciPy

-  `caffe - a fast open framework for deep
   learning <https://github.com/BVLC/caffe>`__

   -  Caffe is a deep learning framework made with expression, speed,
      and modularity in mind

Reinformcement Learning
~~~~~~~~~~~~~~~~~~~~~~~

-  `gym - A toolkit for developing and comparing reinforcement learning
   algorithms <https://github.com/openai/gym>`__

   -  OpenAI Gym is a toolkit for developing and comparing reinforcement
      learning algorithms.
   -  `gym project website <https://gym.openai.com/>`__

Visualization
-------------

-  `PlotNeuralNet - Latex code for making neural networks
   diagrams <https://github.com/HarisIqbal88/PlotNeuralNet>`__

   -  By Haris Iqbal. Latex code for drawing neural networks for reports
      and presentation.