|
21 | 21 | - [Machine Learning](#machine-learning) |
22 | 22 | - [General Purpose Machine Learning](#general-purpose-machine-learning) |
23 | 23 | - [Gradient Boosting](#gradient-boosting) |
24 | | - - [Automated Machine Learning](#automated-machine-learning) |
25 | 24 | - [Ensemble Methods](#ensemble-methods) |
26 | 25 | - [Imbalanced Datasets](#imbalanced-datasets) |
27 | 26 | - [Random Forests](#random-forests) |
|
33 | 32 | - [MXNet](#mxnet) |
34 | 33 | - [JAX](#jax) |
35 | 34 | - [Others](#others) |
| 35 | +- [Automated Machine Learning](#automated-machine-learning) |
36 | 36 | - [Time Series](#time-series) |
37 | 37 | - [Natural Language Processing](#natural-language-processing) |
38 | 38 | - [Computer Audition](#computer-audition) |
|
105 | 105 | * [NGBoost](https://github.com/stanfordmlgroup/ngboost) - Natural Gradient Boosting for Probabilistic Prediction. |
106 | 106 | * [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"> |
107 | 107 |
|
108 | | -### Automated Machine Learning |
109 | | -* [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"> |
110 | | -* [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"> |
111 | | -* [AutoKeras](https://github.com/keras-team/autokeras) - AutoML library for deep learning. <img height="20" src="img/keras_big.png" alt="Keras compatible"> |
112 | | -* [AutoGluon](https://github.com/awslabs/autogluon) - AutoML for Image, Text, Tabular, Time-Series, and MultiModal Data. |
113 | | -* [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"> |
114 | | -* [MLBox](https://github.com/AxeldeRomblay/MLBox) - A powerful Automated Machine Learning python library. |
115 | | - |
116 | 108 | ### Ensemble Methods |
117 | 109 | * [ML-Ensemble](http://ml-ensemble.com/) - High performance ensemble learning. <img height="20" src="img/sklearn_big.png" alt="sklearn"> |
118 | 110 | * [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 | 179 | * [Caffe](https://github.com/BVLC/caffe) - A fast open framework for deep learning. |
188 | 180 | * [nnabla](https://github.com/sony/nnabla) - Neural Network Libraries by Sony. |
189 | 181 |
|
| 182 | +## Automated Machine Learning |
| 183 | +* [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"> |
| 184 | +* [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"> |
| 185 | +* [AutoKeras](https://github.com/keras-team/autokeras) - AutoML library for deep learning. <img height="20" src="img/keras_big.png" alt="Keras compatible"> |
| 186 | +* [AutoGluon](https://github.com/awslabs/autogluon) - AutoML for Image, Text, Tabular, Time-Series, and MultiModal Data. |
| 187 | +* [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"> |
| 188 | +* [MLBox](https://github.com/AxeldeRomblay/MLBox) - A powerful Automated Machine Learning python library. |
| 189 | + |
190 | 190 | ## Time Series |
191 | 191 | * [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"> |
192 | 192 | * [darts](https://github.com/unit8co/darts) - A python library for easy manipulation and forecasting of time series. |
|
0 commit comments