Repository Index

Fast and Accurate ML in 3 Lines of Code

• automl
• machine-learning
• data-science
• deep-learning

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

• machine-learning
• python
• data-science
• automl

AutoML library for deep learning

• automl
• neural-architecture-search
• automated-machine-learning
• python

Automated Machine Learning with scikit-learn

• automl
• scikit-learn
• automated-machine-learning
• hyperparameter-optimization

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

• h2o
• machine-learning
• data-science
• deep-learning

Automatic architecture search and hyperparameter optimization for PyTorch

• automl
• deep-learning
• tabular-data
• pytorch

MLBox is a powerful Automated Machine Learning python library.

• machine-learning
• auto-ml
• kaggle
• deep-learning

SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization

• bayesian-optimization
• bayesian-optimisation
• hyperparameter-optimization
• hyperparameter-tuning

An auto-evolution framework that optimizes anything — your 7×24 team of algorithm engineers.

• evolutionary-algorithms
• rsi-ai
• self-improvement
• llm-agent

Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research.

• python
• pytorch
• machine-learning
• deep-learning

Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-squares, quantile, and expectile regression.

• svm
• regression
• classification
• machine-learning