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20 | 20 | - [Contents](#contents) |
21 | 21 | - [Machine Learning](#machine-learning) |
22 | 22 | - [General Purpose Machine Learning](#general-purpose-machine-learning) |
| 23 | + - [Gradient Boosting](#gradient-boosting) |
23 | 24 | - [Automated Machine Learning](#automated-machine-learning) |
24 | 25 | - [Ensemble Methods](#ensemble-methods) |
25 | 26 | - [Imbalanced Datasets](#imbalanced-datasets) |
26 | 27 | - [Random Forests](#random-forests) |
27 | | - - [Extreme Learning Machine](#extreme-learning-machine) |
28 | 28 | - [Kernel Methods](#kernel-methods) |
29 | | - - [Gradient Boosting](#gradient-boosting) |
| 29 | + - [Extreme Learning Machine](#extreme-learning-machine) |
30 | 30 | - [Deep Learning](#deep-learning) |
31 | 31 | - [PyTorch](#pytorch) |
32 | 32 | - [TensorFlow](#tensorflow) |
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95 | 95 | * [pyGAM](https://github.com/dswah/pyGAM) - Generalized Additive Models in Python. |
96 | 96 | * [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"> |
97 | 97 |
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| 98 | +### Gradient Boosting |
| 99 | +* [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"> |
| 100 | +* [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"> |
| 101 | +* [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"> |
| 102 | +* [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"> |
| 103 | +* [NGBoost](https://github.com/stanfordmlgroup/ngboost) - Natural Gradient Boosting for Probabilistic Prediction. |
| 104 | +* [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"> |
| 105 | + |
98 | 106 | ### Automated Machine Learning |
99 | | -* [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"> |
| 107 | +* [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"> |
100 | 108 | * [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"> |
101 | 109 | * [AutoKeras](https://github.com/keras-team/autokeras) - AutoML library for deep learning. <img height="20" src="img/keras_big.png" alt="Keras compatible"> |
102 | 110 | * [AutoGluon](https://github.com/awslabs/autogluon) - AutoML for Image, Text, Tabular, Time-Series, and MultiModal Data. |
|
118 | 126 | * [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"> |
119 | 127 | * [rgf_python](https://github.com/fukatani/rgf_python) - Python Wrapper of Regularized Greedy Forest. <img height="20" src="img/sklearn_big.png" alt="sklearn"> |
120 | 128 |
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121 | | -### Extreme Learning Machine |
122 | | -* [Python Extreme Learning Machine (ELM)](https://github.com/acba/elm) - A machine learning technique used for classification/regression tasks. |
123 | | -* [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"> |
124 | | - |
125 | 129 | ### Kernel Methods |
126 | 130 | * [pyFM](https://github.com/coreylynch/pyFM) - Factorization machines in python. <img height="20" src="img/sklearn_big.png" alt="sklearn"> |
127 | 131 | * [fastFM](https://github.com/ibayer/fastFM) - A library for Factorization Machines. <img height="20" src="img/sklearn_big.png" alt="sklearn"> |
|
130 | 134 | * [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"> |
131 | 135 | * [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"> |
132 | 136 |
|
133 | | -### Gradient Boosting |
134 | | -* [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"> |
135 | | -* [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"> |
136 | | -* [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"> |
137 | | -* [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"> |
138 | | -* [NGBoost](https://github.com/stanfordmlgroup/ngboost) - Natural Gradient Boosting for Probabilistic Prediction. |
139 | | -* [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"> |
| 137 | +### Extreme Learning Machine |
| 138 | +* [Python Extreme Learning Machine (ELM)](https://github.com/acba/elm) - A machine learning technique used for classification/regression tasks. |
| 139 | +* [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"> |
140 | 140 |
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141 | 141 | ## Deep Learning |
142 | 142 |
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