Update README.md

Author: krzjoa

README.md

@@ -20,13 +20,13 @@
 - [Contents](#contents)
 - [Machine Learning](#machine-learning)
 - [General Purpose Machine Learning](#general-purpose-machine-learning)
+	- [Gradient Boosting](#gradient-boosting)
 - [Automated Machine Learning](#automated-machine-learning)
 - [Ensemble Methods](#ensemble-methods)
 - [Imbalanced Datasets](#imbalanced-datasets)
 - [Random Forests](#random-forests)
-- [Extreme Learning Machine](#extreme-learning-machine)
 - [Kernel Methods](#kernel-methods)
-- [Gradient Boosting](#gradient-boosting)
+ - [Extreme Learning Machine](#extreme-learning-machine)
 - [Deep Learning](#deep-learning)
 - [PyTorch](#pytorch)
 - [TensorFlow](#tensorflow)
@@ -95,8 +95,16 @@
 * [pyGAM](https://github.com/dswah/pyGAM) - Generalized Additive Models in Python.
 * [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">
 
+### Gradient Boosting
+* [XGBoost](https://github.com/dmlc/xgboost) - Scalable, Portable, and Distributed Gradient Boosting. <img height="20" src="img/sklearn_big.png" alt="sklearn"> <img height="20" src="img/gpu_big.png" alt="GPU accelerated">
+* [LightGBM](https://github.com/Microsoft/LightGBM) - A fast, distributed, high-performance gradient boosting. <img height="20" src="img/sklearn_big.png" alt="sklearn"> <img height="20" src="img/gpu_big.png" alt="GPU accelerated">
+* [CatBoost](https://github.com/catboost/catboost) - An open-source gradient boosting on decision trees library. <img height="20" src="img/sklearn_big.png" alt="sklearn"> <img height="20" src="img/gpu_big.png" alt="GPU accelerated">
+* [ThunderGBM](https://github.com/Xtra-Computing/thundergbm) - Fast GBDTs and Random Forests on GPUs. <img height="20" src="img/sklearn_big.png" alt="sklearn"> <img height="20" src="img/gpu_big.png" alt="GPU accelerated">
+* [NGBoost](https://github.com/stanfordmlgroup/ngboost) - Natural Gradient Boosting for Probabilistic Prediction.
+* [TensorFlow Decision Forests](https://github.com/tensorflow/decision-forests) - A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras. <img height="20" src="img/keras_big.png" alt="keras"> <img height="20" src="img/tf_big2.png" alt="TensorFlow">
+
 ### Automated Machine Learning
-* [auto-sklearn](https://github.com/automl/auto-sklearn) - An automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator. <img height="20" src="img/sklearn_big.png" alt="sklearn">
+* [auto-sklearn](https://github.com/automl/auto-sklearn) - An AutoML toolkit and a drop-in replacement for a scikit-learn estimator. <img height="20" src="img/sklearn_big.png" alt="sklearn">
 * [Auto-PyTorch](https://github.com/automl/Auto-PyTorch) - Automatic architecture search and hyperparameter optimization for PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
 * [AutoKeras](https://github.com/keras-team/autokeras) - AutoML library for deep learning. <img height="20" src="img/keras_big.png" alt="Keras compatible">
 * [AutoGluon](https://github.com/awslabs/autogluon) - AutoML for Image, Text, Tabular, Time-Series, and MultiModal Data.
@@ -118,10 +126,6 @@
 * [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">
 
-### Extreme Learning Machine
-* [Python Extreme Learning Machine (ELM)](https://github.com/acba/elm) - A machine learning technique used for classification/regression tasks.
-* [hpelm](https://github.com/akusok/hpelm) - High-performance implementation of Extreme Learning Machines (fast randomized neural networks). <img height="20" src="img/gpu_big.png" alt="GPU accelerated">
-
 ### 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">
@@ -130,13 +134,9 @@
 * [scikit-rvm](https://github.com/JamesRitchie/scikit-rvm) - Relevance Vector Machine implementation using the scikit-learn API. <img height="20" src="img/sklearn_big.png" alt="sklearn">
 * [ThunderSVM](https://github.com/Xtra-Computing/thundersvm) - A fast SVM Library on GPUs and CPUs. <img height="20" src="img/sklearn_big.png" alt="sklearn"> <img height="20" src="img/gpu_big.png" alt="GPU accelerated">
 
-### Gradient Boosting
-* [XGBoost](https://github.com/dmlc/xgboost) - Scalable, Portable, and Distributed Gradient Boosting. <img height="20" src="img/sklearn_big.png" alt="sklearn"> <img height="20" src="img/gpu_big.png" alt="GPU accelerated">
-* [LightGBM](https://github.com/Microsoft/LightGBM) - A fast, distributed, high-performance gradient boosting. <img height="20" src="img/sklearn_big.png" alt="sklearn"> <img height="20" src="img/gpu_big.png" alt="GPU accelerated">
-* [CatBoost](https://github.com/catboost/catboost) - An open-source gradient boosting on decision trees library. <img height="20" src="img/sklearn_big.png" alt="sklearn"> <img height="20" src="img/gpu_big.png" alt="GPU accelerated">
-* [ThunderGBM](https://github.com/Xtra-Computing/thundergbm) - Fast GBDTs and Random Forests on GPUs. <img height="20" src="img/sklearn_big.png" alt="sklearn"> <img height="20" src="img/gpu_big.png" alt="GPU accelerated">
-* [NGBoost](https://github.com/stanfordmlgroup/ngboost) - Natural Gradient Boosting for Probabilistic Prediction.
-* [TensorFlow Decision Forests](https://github.com/tensorflow/decision-forests) - A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras. <img height="20" src="img/keras_big.png" alt="keras"> <img height="20" src="img/tf_big2.png" alt="TensorFlow">
+### Extreme Learning Machine
+* [Python Extreme Learning Machine (ELM)](https://github.com/acba/elm) - A machine learning technique used for classification/regression tasks.
+* [hpelm](https://github.com/akusok/hpelm) - High-performance implementation of Extreme Learning Machines (fast randomized neural networks). <img height="20" src="img/gpu_big.png" alt="GPU accelerated">
 
 ## Deep Learning