Update README.md

Author: krzjoa

README.md

@@ -23,11 +23,11 @@
   	- [Gradient Boosting](#gradient-boosting)
 	- [Ensemble Methods](#ensemble-methods)
 	- [Imbalanced Datasets](#imbalanced-datasets)
-	- [Random Forests](#random-forests)
 	- [Kernel Methods](#kernel-methods)
 - [Deep Learning](#deep-learning)
 	- [PyTorch](#pytorch)
 	- [TensorFlow](#tensorflow)
+    - [Keras](#keras)
  	- [JAX](#jax)
 	- [Others](#others)
 - [Automated Machine Learning](#automated-machine-learning)
@@ -115,11 +115,6 @@
 * [imbalanced-learn](https://github.com/scikit-learn-contrib/imbalanced-learn) - Module to perform under-sampling and over-sampling with various techniques. <img height="20" src="img/sklearn_big.png" alt="sklearn">
 * [imbalanced-algorithms](https://github.com/dialnd/imbalanced-algorithms) - Python-based implementations of algorithms for learning on imbalanced data. <img height="20" src="img/sklearn_big.png" alt="sklearn"> <img height="20" src="img/tf_big2.png" alt="sklearn">
 
-### Random Forests
-* [rpforest](https://github.com/lyst/rpforest) - A forest of random projection trees. <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">
-
 ### Kernel Methods
 * [pyFM](https://github.com/coreylynch/pyFM) - Factorization machines in python. <img height="20" src="img/sklearn_big.png" alt="sklearn">
 * [fastFM](https://github.com/ibayer/fastFM) - A library for Factorization Machines. <img height="20" src="img/sklearn_big.png" alt="sklearn">
@@ -144,24 +139,25 @@
 * [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">
 * [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">
 * [tfdeploy](https://github.com/riga/tfdeploy) - Deploy TensorFlow graphs for fast evaluation and export to TensorFlow-less environments running numpy. <img height="20" src="img/tf_big2.png" alt="sklearn">
 * [tensorflow-upstream](https://github.com/ROCmSoftwarePlatform/tensorflow-upstream) - TensorFlow ROCm port. <img height="20" src="img/tf_big2.png" alt="sklearn"> <img height="20" src="img/amd_big.png" alt="Possible to run on AMD GPU">
 * [TensorFlow Fold](https://github.com/tensorflow/fold) - Deep learning with dynamic computation graphs in TensorFlow. <img height="20" src="img/tf_big2.png" alt="sklearn">
 * [TensorLight](https://github.com/bsautermeister/tensorlight) - A high-level framework for TensorFlow. <img height="20" src="img/tf_big2.png" alt="sklearn">
 * [Mesh TensorFlow](https://github.com/tensorflow/mesh) - Model Parallelism Made Easier. <img height="20" src="img/tf_big2.png" alt="sklearn">
 * [Ludwig](https://github.com/uber/ludwig) - A toolbox that allows one to train and test deep learning models without the need to write code. <img height="20" src="img/tf_big2.png" alt="sklearn">
-* [Keras](https://keras.io) - A high-level neural networks API running on top of TensorFlow.  <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 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">
-* [qkeras](https://github.com/google/qkeras) - A quantization deep learning library. <img height="20" src="img/keras_big.png" alt="Keras compatible">
 
 ### JAX
 * [JAX](https://github.com/google/jax) - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more.
 * [FLAX](https://github.com/google/flax) - A neural network library for JAX that is designed for flexibility.
 * [Optax](https://github.com/google-deepmind/optax) - A gradient processing and optimization library for JAX.
 
+### Keras
+* [Keras](https://keras.io) - A high-level neural networks API running on top of TensorFlow.  <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 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">
+* [qkeras](https://github.com/google/qkeras) - A quantization deep learning library. <img height="20" src="img/keras_big.png" alt="Keras compatible">
+
 ### Others
 * [transformers](https://github.com/huggingface/transformers) - State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible"> <img height="20" src="img/tf_big2.png" alt="sklearn">
 * [Tangent](https://github.com/google/tangent) - Source-to-Source Debuggable Derivatives in Pure Python.
@@ -276,7 +272,7 @@
 * [GreatX](https://github.com/EdisonLeeeee/GreatX) - A graph reliability toolbox based on PyTorch and PyTorch Geometric (PyG). <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
 * [Jraph](https://github.com/google-deepmind/jraph) - A Graph Neural Network Library in Jax.
 * [TRL](https://github.com/huggingface/trl) - Train transformer language models with reinforcement learning.
-  
+* [Cleora](https://github.com/BaseModelAI/cleora) - The Graph Embedding Engine.
 
 ## Graph Manipulation
 * [Networkx](https://github.com/networkx/networkx) - Network Analysis in Python.
@@ -315,7 +311,6 @@
 * [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">
 
 ## Model Explanation
-
 * [dalex](https://github.com/ModelOriented/DALEX) - moDel Agnostic Language for Exploration and explanation. <img height="20" src="img/sklearn_big.png" alt="sklearn"><img height="20" src="img/R_big.png" alt="R inspired/ported lib">
 * [Shapley](https://github.com/benedekrozemberczki/shapley) - A data-driven framework to quantify the value of classifiers in a machine learning ensemble.
 * [Alibi](https://github.com/SeldonIO/alibi) - Algorithms for monitoring and explaining machine learning models.