The Best cl-online-learning Alternatives in 2026

This repository implements random forest models in Common Lisp for classification tasks.

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

numpy-ml collects machine learning algorithms in NumPy, presented as an intentionally readable Python package.

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

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

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

Karate Club offers algorithms for unsupervised learning on graph data in Python.

metric-learn provides Python implementations of metric learning algorithms.

eaopt provides Go libraries for genetic algorithms, particle swarm optimization, and differential evolution.

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

pomegranate provides flexible probabilistic modeling tools and algorithms for Python users.

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.

neural-fortran provides a parallel framework for deep learning in Fortran.

Katib automates machine learning on Kubernetes, including hyperparameter tuning and neural architecture search.

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.