Repository Index

Open-source, low-code AutoML platform for Python. PyCaret 4.0: sklearn-native engine + React control plane.

• data-science
• python
• machine-learning
• pycaret

A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.

• automl
• hyperparam
• automated-machine-learning
• machine-learning

Scalable and user friendly neural :brain: forecasting algorithms.

• deep-learning
• forecasting
• esrnn
• nbeats

Differentiable architecture search for convolutional and recurrent networks

• deep-learning
• automl
• image-classification
• language-modeling

Python AutoML toolkit for benchmarking classification, regression, and time-series forecasting models, with GPU acceleration, MLflow tracking, and automated preprocessing.

• machine-learning
• automl
• regression
• classification

An Automatic Model Compression (AutoMC) framework for developing smaller and faster AI applications.

• deep-learning
• model-compression
• mobile-app
• automl

TransmogrifAI (pronounced trăns-mŏgˈrə-fī) is an AutoML library for building modular, reusable, strongly typed machine learning workflows on Apache Spark with minimal hand-tuning

• ml
• automl
• transformations
• estimators

Python AutoML for Trading Systems and Sports Betting

• machine-learning
• predictive-analytics
• classification
• regression

OpenMMLab Model Compression Toolbox and Benchmark.

• nas
• pruning
• knowledge-distillation
• spos

Machine Learning in R

• machine-learning
• data-science
• tuning
• cran

MLBox is a powerful Automated Machine Learning python library.

• machine-learning
• auto-ml
• kaggle
• 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