ML Training & Optimization

This repository includes examples and a guide for fine-tuning Stable Diffusion models.

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

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

DeepSpeed provides optimization tools that make distributed deep learning training more efficient.

Datature supports data labeling, computer-vision model training, and deployment for enterprise and developer teams.

Octo provides a transformer-based policy for robots, with code to train and fine-tune it.

DreamBooth describes fine-tuning text-to-image diffusion models to generate images of specific subjects.

The Accord.MachineLearning package includes .NET algorithms for support vector machines, decision trees, and clustering.

Knet provides a deep learning framework developed at Koç University for building models.

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

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

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

Aerosolve provides a machine learning package with example models and documentation.

hyperopt-sklearn uses hyperparameter optimization to select machine learning algorithms for scikit-learn.

ViZDoom provides reinforcement learning environments where AI bots play Doom using screen information.

JAX composes transformations for Python and NumPy programs, including differentiation, vectorization, and compilation.

ML-Ensemble provides Python tools for high-performance ensemble learning with scikit-learn.

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

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

Archai supports neural architecture search with fast, reproducible, and modular research tools.

emel provides functional machine learning tools for developers using the Erlang ecosystem.

The Neuron library implements linear, quadratic, radial-basis, and multilayer neural network units.

AutoGL provides automated graph learning through a machine learning framework and toolkit.

Lightwood builds and customizes machine learning pipelines using a declarative JSON-AI syntax.