Update README.md

Author: krzjoa

README.md

@@ -32,7 +32,10 @@
 - [TensorFlow](#tensorflow)
 - [MXNet](#mxnet)
 - [Others](#others)
-- [Web Scraping](#web-scraping)
+- [Reinforcement Learning](#reinforcement-learning)
+- [Graph Machine Learning](#graph-machine-learninh)
+- [Probabilistic Graphical Models](#probabilistic-graphical-models)
+- [Probabilistic Methods](#probabilistic-methods)
 - [Data Manipulation](#data-manipulation)
 - [Data Frames](#data-frames)
 - [Pipelines](#pipelines)
@@ -49,8 +52,6 @@
 - [NLP](#nlp)
 - [Deployment](#deployment)
 - [Model Explanation](#model-explanation)
-- [Reinforcement Learning](#reinforcement-learning)
-- [Probabilistic Methods](#probabilistic-methods)
 - [Genetic Programming](#genetic-programming)
 - [Optimization](#optimization)
 - [Time Series](#time-series)
@@ -63,6 +64,7 @@
 - [Data Validation](#data-validation)
 - [Evaluation](#evaluation)
 - [Computations](#computations)
+- [Web Scraping](#web-scraping)
 - [Spatial Analysis](#spatial-analysis)
 - [Quantum Computing](#quantum-computing)
 - [Conversion](#conversion)
@@ -91,8 +93,6 @@
 * [RuleFit](https://github.com/christophM/rulefit) - Implementation of the rulefit. <img height="20" src="img/sklearn_big.png" alt="sklearn">
 * [metric-learn](https://github.com/all-umass/metric-learn) - Metric learning algorithms in Python. <img height="20" src="img/sklearn_big.png" alt="sklearn">
 * [pyGAM](https://github.com/dswah/pyGAM) - Generalized Additive Models in Python.
-* [Karate Club](https://github.com/benedekrozemberczki/karateclub) - An unsupervised machine learning library for graph-structured data.
-* [Little Ball of Fur](https://github.com/benedekrozemberczki/littleballoffur) - A library for sampling graph structured data.
 * [causalml](https://github.com/uber/causalml) - Uplift modeling and causal inference with machine learning algorithms. <img height="20" src="img/sklearn_big.png" alt="sklearn">
 
 ### Automated Machine Learning
@@ -145,9 +145,7 @@
 * [torchaudio](https://github.com/pytorch/audio) - An audio library for PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
 * [ignite](https://github.com/pytorch/ignite) - High-level library to help with training neural networks in PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
 * [skorch](https://github.com/dnouri/skorch) - A scikit-learn compatible neural network library that wraps PyTorch. <img height="20" src="img/sklearn_big.png" alt="sklearn"> <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
-* [pytorch_geometric](https://github.com/rusty1s/pytorch_geometric) - Geometric Deep Learning Extension Library for PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
 * [Catalyst](https://github.com/catalyst-team/catalyst) - High-level utils for PyTorch DL & RL research. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
-* [pytorch_geometric_temporal](https://github.com/benedekrozemberczki/pytorch_geometric_temporal) - Temporal Extension Library for PyTorch Geometric. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
 * [ChemicalX](https://github.com/AstraZeneca/chemicalx) - A PyTorch-based deep learning library for drug pair scoring. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
 
 ### TensorFlow
@@ -168,7 +166,6 @@
 * [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 straightforward wrapper for a 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">
-* [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">
 
 ### MXNet
@@ -189,14 +186,49 @@
 * [Caffe](https://github.com/BVLC/caffe) - A fast open framework for deep learning.
 * [hipCaffe](https://github.com/ROCmSoftwarePlatform/hipCaffe) - The HIP port of Caffe. <img height="20" src="img/amd_big.png" alt="Possible to run on AMD GPU">
 
-**[DISCONTINUED PROJECTS](https://github.com/krzjoa/awesome-python-data-science/blob/master/other/deprecated.md#deep-learning)**
+## Reinforcement Learning
+* [Gymnasium](https://github.com/Farama-Foundation/Gymnasium) - An API standard for single-agent reinforcement learning environments, with popular reference environments and related utilities (formerly [Gym](https://github.com/openai/gym)).
+* [Stable Baselines3](https://github.com/DLR-RM/stable-baselines3) - 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.
+* [Acme](https://github.com/google-deepmind/acme) - A library of reinforcement learning components and agents.
+* [Catalyst-RL](https://github.com/catalyst-team/catalyst-rl) - PyTorch framework for RL research.
+* [d3rlpy](https://github.com/takuseno/d3rlpy) - An offline deep reinforcement learning library.
+* [Tianshou](https://github.com/thu-ml/tianshou/#comprehensive-functionality) - An elegant PyTorch deep reinforcement learning library.
+* [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">
+* [garage](https://github.com/rlworkgroup/garage) - A toolkit for reproducible reinforcement learning research.
+* [Horizon](https://github.com/facebookresearch/Horizon) - A platform for Applied Reinforcement Learning.
+
+## Graph Machine Learning
+* [pytorch_geometric](https://github.com/rusty1s/pytorch_geometric) - Geometric Deep Learning Extension Library for PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
+* [pytorch_geometric_temporal](https://github.com/benedekrozemberczki/pytorch_geometric_temporal) - Temporal Extension Library for PyTorch Geometric. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
+* [Spektral](https://github.com/danielegrattarola/spektral) - Deep learning on graphs. <img height="20" src="img/keras_big.png" alt="Keras compatible">
+* [Karate Club](https://github.com/benedekrozemberczki/karateclub) - An unsupervised machine learning library for graph-structured data.
+* [Little Ball of Fur](https://github.com/benedekrozemberczki/littleballoffur) - A library for sampling graph structured data.
 
-## Web Scraping
-* [BeautifulSoup](https://www.crummy.com/software/BeautifulSoup/bs4/doc/): The easiest library to scrape static websites for beginners
-* [Scrapy](https://scrapy.org/): Fast and extensible scraping library. Can write rules and create customized scraper without touching the core
-* [Selenium](https://selenium-python.readthedocs.io/installation.html#introduction): Use Selenium Python API to access all functionalities of Selenium WebDriver in an intuitive way like a real user.
-* [Pattern](https://github.com/clips/pattern): High level scraping for well-establish websites such as Google, Twitter, and Wikipedia. Also has NLP, machine learning algorithms, and visualization
-* [twitterscraper](https://github.com/taspinar/twitterscraper): Efficient library to scrape Twitter
+## Probabilistic Graphical Models
+* [pomegranate](https://github.com/jmschrei/pomegranate) - Probabilistic and graphical models for Python. <img height="20" src="img/gpu_big.png" alt="GPU accelerated">
+* [pgmpy](https://github.com/pgmpy/pgmpy) - A python library for working with Probabilistic Graphical Models.
+* [pyAgrum](https://agrum.gitlab.io/) - A GRaphical Universal Modeler.
+
+## Probabilistic Methods
+* [pyro](https://github.com/uber/pyro) - A flexible, scalable deep probabilistic programming library built on PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
+* [PyMC](https://github.com/pymc-devs/pymc) - Bayesian Stochastic Modelling in Python.
+* [ZhuSuan](http://zhusuan.readthedocs.io/en/latest/) - Bayesian Deep Learning. <img height="20" src="img/tf_big2.png" alt="sklearn">
+* [GPflow](http://gpflow.readthedocs.io/en/latest/?badge=latest) - Gaussian processes in TensorFlow. <img height="20" src="img/tf_big2.png" alt="sklearn">
+* [InferPy](https://github.com/PGM-Lab/InferPy) - Deep Probabilistic Modelling Made Easy. <img height="20" src="img/tf_big2.png" alt="sklearn">
+* [PyStan](https://github.com/stan-dev/pystan) - Bayesian inference using the No-U-Turn sampler (Python interface).
+* [sklearn-bayes](https://github.com/AmazaspShumik/sklearn-bayes) - Python package for Bayesian Machine Learning with scikit-learn API. <img height="20" src="img/sklearn_big.png" alt="sklearn">
+* [skpro](https://github.com/alan-turing-institute/skpro) - Supervised domain-agnostic prediction framework for probabilistic modelling by [The Alan Turing Institute](https://www.turing.ac.uk/). <img height="20" src="img/sklearn_big.png" alt="sklearn">
+* [PyVarInf](https://github.com/ctallec/pyvarinf) - Bayesian Deep Learning methods with Variational Inference for PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
+* [emcee](https://github.com/dfm/emcee) - The Python ensemble sampling toolkit for affine-invariant MCMC.
+* [hsmmlearn](https://github.com/jvkersch/hsmmlearn) - A library for hidden semi-Markov models with explicit durations.
+* [pyhsmm](https://github.com/mattjj/pyhsmm) - Bayesian inference in HSMMs and HMMs.
+* [GPyTorch](https://github.com/cornellius-gp/gpytorch) - A highly efficient and modular implementation of Gaussian Processes in PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
+* [sklearn-crfsuite](https://github.com/TeamHG-Memex/sklearn-crfsuite) - A scikit-learn-inspired API for CRFsuite. <img height="20" src="img/sklearn_big.png" alt="sklearn">
 
 ## Data Manipulation
 
@@ -329,42 +361,7 @@
 * [tensorboard-pytorch](https://github.com/lanpa/tensorboard-pytorch) - Tensorboard for PyTorch (and chainer, mxnet, numpy, ...).
 * [mxboard](https://github.com/awslabs/mxboard) - Logging MXNet data for visualization in TensorBoard. <img height="20" src="img/mxnet_big.png" alt="MXNet based">
 
-## Reinforcement Learning
-* [Gymnasium](https://github.com/Farama-Foundation/Gymnasium) - An API standard for single-agent reinforcement learning environments, with popular reference environments and related utilities (formerly [Gym](https://github.com/openai/gym)).
-* [Stable Baselines3](https://github.com/DLR-RM/stable-baselines3) - 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.
-* [Acme](https://github.com/google-deepmind/acme) - A library of reinforcement learning components and agents.
-* [Catalyst-RL](https://github.com/catalyst-team/catalyst-rl) - PyTorch framework for RL research.
-* [d3rlpy](https://github.com/takuseno/d3rlpy) - An offline deep reinforcement learning library.
-* [Tianshou](https://github.com/thu-ml/tianshou/#comprehensive-functionality) - An elegant PyTorch deep reinforcement learning library.
-* [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">
-* [garage](https://github.com/rlworkgroup/garage) - A toolkit for reproducible reinforcement learning research.
-* [Horizon](https://github.com/facebookresearch/Horizon) - A platform for Applied Reinforcement Learning.
-
-## Probabilistic Graphical Models
-* [pomegranate](https://github.com/jmschrei/pomegranate) - Probabilistic and graphical models for Python. <img height="20" src="img/gpu_big.png" alt="GPU accelerated">
-* [pgmpy](https://github.com/pgmpy/pgmpy) - A python library for working with Probabilistic Graphical Models.
-* [pyAgrum](https://agrum.gitlab.io/) - A GRaphical Universal Modeler.
 
-## Probabilistic Methods
-* [pyro](https://github.com/uber/pyro) - A flexible, scalable deep probabilistic programming library built on PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
-* [PyMC](https://github.com/pymc-devs/pymc) - Bayesian Stochastic Modelling in Python.
-* [ZhuSuan](http://zhusuan.readthedocs.io/en/latest/) - Bayesian Deep Learning. <img height="20" src="img/tf_big2.png" alt="sklearn">
-* [GPflow](http://gpflow.readthedocs.io/en/latest/?badge=latest) - Gaussian processes in TensorFlow. <img height="20" src="img/tf_big2.png" alt="sklearn">
-* [InferPy](https://github.com/PGM-Lab/InferPy) - Deep Probabilistic Modelling Made Easy. <img height="20" src="img/tf_big2.png" alt="sklearn">
-* [PyStan](https://github.com/stan-dev/pystan) - Bayesian inference using the No-U-Turn sampler (Python interface).
-* [sklearn-bayes](https://github.com/AmazaspShumik/sklearn-bayes) - Python package for Bayesian Machine Learning with scikit-learn API. <img height="20" src="img/sklearn_big.png" alt="sklearn">
-* [skpro](https://github.com/alan-turing-institute/skpro) - Supervised domain-agnostic prediction framework for probabilistic modelling by [The Alan Turing Institute](https://www.turing.ac.uk/). <img height="20" src="img/sklearn_big.png" alt="sklearn">
-* [PyVarInf](https://github.com/ctallec/pyvarinf) - Bayesian Deep Learning methods with Variational Inference for PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
-* [emcee](https://github.com/dfm/emcee) - The Python ensemble sampling toolkit for affine-invariant MCMC.
-* [hsmmlearn](https://github.com/jvkersch/hsmmlearn) - A library for hidden semi-Markov models with explicit durations.
-* [pyhsmm](https://github.com/mattjj/pyhsmm) - Bayesian inference in HSMMs and HMMs.
-* [GPyTorch](https://github.com/cornellius-gp/gpytorch) - A highly efficient and modular implementation of Gaussian Processes in PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
-* [sklearn-crfsuite](https://github.com/TeamHG-Memex/sklearn-crfsuite) - A scikit-learn-inspired API for CRFsuite. <img height="20" src="img/sklearn_big.png" alt="sklearn">
 
 ## Genetic Programming
 * [gplearn](https://github.com/trevorstephens/gplearn) - Genetic Programming in Python. <img height="20" src="img/sklearn_big.png" alt="sklearn">
@@ -495,6 +492,13 @@
 * [adaptive](https://github.com/python-adaptive/adaptive) - Tools for adaptive and parallel samping of mathematical functions.
 * [NumExpr](https://github.com/pydata/numexpr) - A fast numerical expression evaluator for NumPy that comes with an integrated computing virtual machine to speed calculations up by avoiding memory allocation for intermediate results.
 
+## Web Scraping
+* [BeautifulSoup](https://www.crummy.com/software/BeautifulSoup/bs4/doc/): The easiest library to scrape static websites for beginners
+* [Scrapy](https://scrapy.org/): Fast and extensible scraping library. Can write rules and create customized scraper without touching the core
+* [Selenium](https://selenium-python.readthedocs.io/installation.html#introduction): Use Selenium Python API to access all functionalities of Selenium WebDriver in an intuitive way like a real user.
+* [Pattern](https://github.com/clips/pattern): High level scraping for well-establish websites such as Google, Twitter, and Wikipedia. Also has NLP, machine learning algorithms, and visualization
+* [twitterscraper](https://github.com/taspinar/twitterscraper): Efficient library to scrape Twitter
+
 ## Spatial Analysis
 * [GeoPandas](https://github.com/geopandas/geopandas) - Python tools for geographic data. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
 * [PySal](https://github.com/pysal/pysal) - Python Spatial Analysis Library.