The Best cl-random-forest Alternatives in 2026

This Common Lisp repository collects algorithms for online linear classification.

mgl provides a machine learning library in Common Lisp, including neural networks and Gaussian processes.

This repository implements DreamBooth subject fine-tuning for Stable Diffusion using textual inversion.

BayesO provides a Python package for Bayesian optimization algorithms and related modeling tasks.

autoai automates regression and classification modeling in Python through model search and hyperparameter tuning.

This metric-learn repository contains Python implementations of metric learning algorithms.

PyTorch Lightning helps pretrain and fine-tune AI models across one or many GPUs with minimal code changes.

Use Learnergy, a Python library for training and working with energy-based machine learning models.

PyTorch Lightning extension that accelerates and enhances foundation model experimentation with flexible fine-tuning schedules.

TensorFlow Model Optimization provides Keras and TensorFlow tools for model quantization and pruning.

doddle-model is an in-memory machine learning library for Scala, built on Breeze with a scikit-learn-like API using typeclasses.

AutoGluon provides tools for automated machine learning and model development.

FEDOT automates machine learning model development through its modeling framework.

Enables single machine or distributed training and evaluation of deep learning models.

Detecto supports building and training computer vision models with the PyTorch machine learning framework.

ML_Forgex trains, evaluates, and predicts with machine learning models on cleaned datasets.

PyTorch Geometric Temporal provides models and tools for spatiotemporal signal processing with neural learning.

Auto_ViML builds multiple machine learning models with a single line of code.

DeepSpeed optimizes distributed training and inference for deep learning models.

Determined manages distributed model training, hyperparameter tuning, experiment tracking, and resources.

m2cgen converts machine learning models into native code for multiple programming languages without runtime dependencies.

Bender helps developers build neural networks for iOS using TensorFlow models and Metal.

KerasTuner searches hyperparameters to optimize machine learning models built with Keras.

AutoKeras automates development of deep learning models through a machine learning library.