Update README.md

README.md

@@ -21,7 +21,6 @@
 - [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)
@@ -33,6 +32,7 @@
 	- [MXNet](#mxnet)
  	- [JAX](#jax)
 	- [Others](#others)
+- [Automated Machine Learning](#automated-machine-learning)
 - [Time Series](#time-series)
 - [Natural Language Processing](#natural-language-processing)
 - [Computer Audition](#computer-audition)
@@ -105,14 +105,6 @@
 * [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 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.
-* [TPOT](https://github.com/rhiever/tpot) - AutoML tool that optimizes machine learning pipelines using genetic programming. <img height="20" src="img/sklearn_big.png" alt="sklearn">
-* [MLBox](https://github.com/AxeldeRomblay/MLBox) - A powerful Automated Machine Learning python library.
-
 ### Ensemble Methods
 * [ML-Ensemble](http://ml-ensemble.com/) - High performance ensemble learning. <img height="20" src="img/sklearn_big.png" alt="sklearn">
 * [Stacking](https://github.com/ikki407/stacking) - Simple and useful stacking library written in Python. <img height="20" src="img/sklearn_big.png" alt="sklearn">
@@ -187,6 +179,14 @@
 * [Caffe](https://github.com/BVLC/caffe) - A fast open framework for deep learning.
 * [nnabla](https://github.com/sony/nnabla) - Neural Network Libraries by Sony.
 
+## Automated Machine Learning
+* [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.
+* [TPOT](https://github.com/rhiever/tpot) - AutoML tool that optimizes machine learning pipelines using genetic programming. <img height="20" src="img/sklearn_big.png" alt="sklearn">
+* [MLBox](https://github.com/AxeldeRomblay/MLBox) - A powerful Automated Machine Learning python library.
+
 ## Time Series
 * [sktime](https://github.com/alan-turing-institute/sktime) - A unified framework for machine learning with time series. <img height="20" src="img/sklearn_big.png" alt="sklearn">
 * [darts](https://github.com/unit8co/darts) - A python library for easy manipulation and forecasting of time series.