Website improved

Author: Krzysztof Joachimiak

docs/README.md

@@ -24,14 +24,14 @@
 * [Visualization](#vis)
 * [Model Explanation](#expl)
 * [Reinforcement Learning](#rl)
-* [Distributed Computing](#dist)
 * [Probabilistic Methods](#bayes)
 * [Genetic Programming](#gp)
 * [Optimization](#opt)
 * [Natural Language Processing](#nlp)
 * [Computer Audition](#ca)
 * [Computer Vision](#cv)
 * [Statistics](#stat)
+* [Distributed Computing](#dist)
 * [Experimentation](#tools)
 * [Evaluation](#eval)
 * [Computations](#compt)
@@ -92,7 +92,6 @@
 <a name="ml-rf"></a>
 ### Random Forests
 * [rpforest](https://github.com/lyst/rpforest) - A forest of random projection trees. <img height="20" src="img/sklearn_big.png" alt="sklearn">
-* [Random Forest Clustering](https://github.com/joshloyal/RandomForestClustering) - Unsupervised Clustering using Random Forests.<img height="20" src="img/sklearn_big.png" alt="sklearn">
 * [sklearn-random-bits-forest](https://github.com/tmadl/sklearn-random-bits-forest) - Wrapper of the Random Bits Forest program written by (Wang et al., 2016).<img height="20" src="img/sklearn_big.png" alt="sklearn">
 * [rgf_python](https://github.com/fukatani/rgf_python) - Python Wrapper of Regularized Greedy Forest. <img height="20" src="img/sklearn_big.png" alt="sklearn">
 
@@ -140,7 +139,6 @@
 * [TensorLayer](https://github.com/zsdonghao/tensorlayer) - Deep Learning and Reinforcement Learning Library for Researcher and Engineer. <img height="20" src="img/tf_big2.png" alt="sklearn">
 * [TFLearn](https://github.com/tflearn/tflearn) - Deep learning library featuring a higher-level API for TensorFlow. <img height="20" src="img/tf_big2.png" alt="sklearn">
 * [Sonnet](https://github.com/deepmind/sonnet) - TensorFlow-based neural network library. <img height="20" src="img/tf_big2.png" alt="sklearn">
-* [TensorForce](https://github.com/reinforceio/tensorforce) - A TensorFlow library for applied reinforcement learning. <img height="20" src="img/tf_big2.png" alt="sklearn">
 * [tensorpack](https://github.com/ppwwyyxx/tensorpack) - A Neural Net Training Interface on TensorFlow <img height="20" src="img/tf_big2.png" alt="sklearn">
 * [Polyaxon](https://github.com/polyaxon/polyaxon) - A platform that helps you build, manage and monitor deep learning models. <img height="20" src="img/tf_big2.png" alt="sklearn">
 * [NeuPy](https://github.com/itdxer/neupy) - NeuPy is a Python library for Artificial Neural Networks and Deep Learning (previously: <img height="20" src="img/theano_big.png" alt="Theano compatible">). <img height="20" src="img/tf_big2.png" alt="sklearn">
@@ -154,13 +152,13 @@
 
 <a name="dl-keras"></a>
 ### Keras
-* [Keras](https://keras.io) - A high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano.
-* [keras-contrib](https://github.com/keras-team/keras-contrib) - Keras community contributions.
-* [Hyperas](https://github.com/maxpumperla/hyperas) - Keras + Hyperopt: A very simple wrapper for convenient hyperparameter.
-* [Elephas](https://github.com/maxpumperla/elephas) - Distributed Deep learning with Keras & Spark.
-* [Hera](https://github.com/keplr-io/hera) - Train/evaluate a Keras model, get metrics streamed to a dashboard in your browser.
-* [Spektral](https://github.com/danielegrattarola/spektral) - Deep learning on graphs.
-* [qkeras](https://github.com/google/qkeras) - A quantization deep learning library.
+* [Keras](https://keras.io) - A high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano.  <img height="20" src="img/keras_big.png" alt="Keras compatible">
+* [keras-contrib](https://github.com/keras-team/keras-contrib) - Keras community contributions. <img height="20" src="img/keras_big.png" alt="Keras compatible">
+* [Hyperas](https://github.com/maxpumperla/hyperas) - Keras + Hyperopt: A very simple wrapper for convenient hyperparameter. <img height="20" src="img/keras_big.png" alt="Keras compatible">
+* [Elephas](https://github.com/maxpumperla/elephas) - Distributed Deep learning with Keras & Spark. <img height="20" src="img/keras_big.png" alt="Keras compatible">
+* [Hera](https://github.com/keplr-io/hera) - Train/evaluate a Keras model, get metrics streamed to a dashboard in your browser. <img height="20" src="img/keras_big.png" alt="Keras compatible">
+* [Spektral](https://github.com/danielegrattarola/spektral) - Deep learning on graphs. <img height="20" src="img/keras_big.png" alt="Keras compatible">
+* [qkeras](https://github.com/google/qkeras) - A quantization deep learning library. <img height="20" src="img/keras_big.png" alt="Keras compatible">
 
 <a name="dl-mxnet"></a>
 ### MXNet
@@ -176,7 +174,6 @@
 <a name="dl-chainer"></a>
 ### Chainer
 * [Chainer](https://github.com/chainer/chainer) - A flexible framework for neural networks.
-* [ChainerRL](https://github.com/chainer/chainerrl) - A deep reinforcement learning library built on top of Chainer.
 * [ChainerCV](https://github.com/chainer/chainercv) - A Library for Deep Learning in Computer Vision.
 * [ChainerMN](https://github.com/chainer/chainermn) - Scalable distributed deep learning with Chainer.
 
@@ -233,7 +230,6 @@
 * [meza](https://github.com/reubano/meza) - A Python toolkit for processing tabular data.
 * [Prodmodel](https://github.com/prodmodel/prodmodel) - Build system for data science pipelines.
 
-
 <a name="feat-eng"></a>
 ## Feature Engineering
 
@@ -262,6 +258,9 @@
 * [prettyplotlib](https://github.com/olgabot/prettyplotlib) - Painlessly create beautiful matplotlib plots.
 * [python-ternary](https://github.com/marcharper/python-ternary) - Ternary plotting library for python with matplotlib.
 * [missingno](https://github.com/ResidentMario/missingno) - Missing data visualization module for Python.
+* [chartify](https://github.com/spotify/chartify/) - Python library that makes it easy for data scientists to create charts.
+* [physt](https://github.com/janpipek/physt) - Improved histograms.
+* [animatplot](https://github.com/t-makaro/animatplot) - A python package for animating plots build on matplotlib.
 
 <a name="expl"></a>
 ## Model Explanation
@@ -295,6 +294,7 @@
 <a name="rl"></a>
 ## Reinforcement Learning
 * [OpenAI Gym](https://github.com/openai/gym) - A toolkit for developing and comparing reinforcement learning algorithms.
+<<<<<<< HEAD
 
 <a name="dist"></a>
 ## Distributed Computing
@@ -306,6 +306,20 @@
 * [PaddlePaddle](https://github.com/PaddlePaddle/Paddle) - PArallel Distributed Deep LEarning.
 * [dask-ml](https://github.com/dask/dask-ml) - Distributed and parallel machine learning. <img height="20" src="img/sklearn_big.png" alt="sklearn">
 * [Distributed](https://github.com/dask/distributed) - Distributed computation in Python.
+=======
+* [Coach](https://github.com/NervanaSystems/coach) - Easy experimentation with state of the art Reinforcement Learning algorithms.
+* [garage](https://github.com/rlworkgroup/garage) - A toolkit for reproducible reinforcement learning research.
+* [OpenAI Baselines](https://github.com/openai/baselines) - High-quality implementations of reinforcement learning algorithms.
+* [Stable Baselines](https://github.com/hill-a/stable-baselines) - A set of improved implementations of reinforcement learning algorithms based on OpenAI Baselines.
+* [RLlib](https://ray.readthedocs.io/en/latest/rllib.html) - Scalable Reinforcement Learning.
+* [Horizon](https://github.com/facebookresearch/Horizon) - A platform for Applied Reinforcement Learning.
+* [TF-Agents](https://github.com/tensorflow/agents) - A library for Reinforcement Learning in TensorFlow. <img height="20" src="img/tf_big2.png" alt="sklearn">  
+* [TensorForce](https://github.com/reinforceio/tensorforce) - A TensorFlow library for applied reinforcement learning. <img height="20" src="img/tf_big2.png" alt="sklearn">
+* [TRFL](https://github.com/deepmind/trfl) - TensorFlow Reinforcement Learning. <img height="20" src="img/tf_big2.png" alt="sklearn">
+* [Dopamine](https://github.com/google/dopamine) - A research framework for fast prototyping of reinforcement learning algorithms.
+* [keras-rl](https://github.com/keras-rl/keras-rl) - Deep Reinforcement Learning for Keras. <img height="20" src="img/keras_big.png" alt="Keras compatible">
+* [ChainerRL](https://github.com/chainer/chainerrl) - A deep reinforcement learning library built on top of Chainer.
+>>>>>>> a2d28c6bc10e869f370df253130e94e424371c6f
 
 <a name="bayes"></a>
 ## Probabilistic Methods
@@ -405,6 +419,17 @@
 * [scikit-posthocs](https://github.com/maximtrp/scikit-posthocs) - Pairwise Multiple Comparisons Post-hoc Tests.
 * [Alphalens](https://github.com/quantopian/alphalens) - Performance analysis of predictive (alpha) stock factors.
 
+<a name="dist"></a>
+## Distributed Computing
+* [Horovod](https://github.com/uber/horovod) - Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet. <img height="20" src="img/tf_big2.png" alt="sklearn">
+* [PySpark](https://spark.apache.org/docs/0.9.0/python-programming-guide.html) - Exposes the Spark programming model to Python. <img height="20" src="img/spark_big.png" alt="Apache Spark based">
+* [Veles](https://github.com/Samsung/veles) - Distributed machine learning platform.
+* [Jubatus](https://github.com/jubatus/jubatus) - Framework and Library for Distributed Online Machine Learning.
+* [DMTK](https://github.com/Microsoft/DMTK) - Microsoft Distributed Machine Learning Toolkit.
+* [PaddlePaddle](https://github.com/PaddlePaddle/Paddle) - PArallel Distributed Deep LEarning
+* [dask-ml](https://github.com/dask/dask-ml) - Distributed and parallel machine learning. <img height="20" src="img/sklearn_big.png" alt="sklearn">
+* [Distributed](https://github.com/dask/distributed) - Distributed computation in Python.
+
 <a name="tools"></a>
 ## Experimentation
 * [Sacred](https://github.com/IDSIA/sacred) - A tool to help you configure, organize, log and reproduce experiments.
@@ -424,7 +449,6 @@
 * [numpy](http://www.numpy.org/) - The fundamental package needed for scientific computing with Python.
 * [Dask](https://github.com/dask/dask) - Parallel computing with task scheduling. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
 * [bottleneck](https://github.com/kwgoodman/bottleneck) - Fast NumPy array functions written in C.
-* [minpy](https://github.com/dmlc/minpy) - NumPy interface with mixed backend execution.
 * [CuPy](https://github.com/cupy/cupy) - NumPy-like API accelerated with CUDA.
 * [scikit-tensor](https://github.com/mnick/scikit-tensor) - Python library for multilinear algebra and tensor factorizations.
 * [numdifftools](https://github.com/pbrod/numdifftools) - Solve automatic numerical differentiation problems in one or more variables.
@@ -438,6 +462,7 @@
 
 <a name="quant"></a>
 ## Quantum Computing
+* [PennyLane](https://github.com/XanaduAI/pennylane) - Quantum machine learning, automatic differentiation, and optimization of hybrid quantum-classical computations.
 * [QML](https://github.com/qmlcode/qml) - A Python Toolkit for Quantum Machine Learning.
 
 <a name="conv"></a>

docs/index.html

@@ -3,7 +3,7 @@
 <head>
   <meta charset="UTF-8">
   <title>Awesome Python Data Science</title>
-   <link rel="icon" type="image/png" href="img/py-datascience.png">
+  <link rel="icon" type="image/png" href="img/py-datascience.png">
   <meta http-equiv="X-UA-Compatible" content="IE=edge,chrome=1" />
   <meta name="description" content="Description">
   <meta name="viewport" content="width=device-width, user-scalable=no, initial-scale=1.0, maximum-scale=1.0, minimum-scale=1.0">