The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.

Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

Tree-of-Parzen-estimators hyperparameter optimization

Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.

Nomadic is an enterprise-grade framework for teams to continuously optimize compound AI systems

SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization

Automated Machine Learning with scikit-learn

Library for Semi-Automated Data Science

Automate machine learning tasks at the code level with LLMs and autoML | Based on the TMLR paper "Large Language Models Synergize with Automated Machine Learning"

Black-box optimization library

A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.

OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)

🔨 Malet (Machine Learning Experiment Tool) is a tool for efficient machine learning experiment execution, logging, analysis, and plot making.

This project focuses on predicting the weather for the next day using a classification model. Both RandomForest and GradientBoosting classifiers were tested with grid search for hyperparameter tuning. The dataset used for this project is available at Kaggle.

This repository is the code basis for the paper intitled "Unlocking Neural Networks: Hyperparameter Analysis"

Sequential model-based optimization with a `scipy.optimize` interface

Hyperparameter selection on machine learning models using Particle Swarm Optimization

An AutoRecSys library for Surprise. Automate algorithm selection and hyperparameter tuning 🚀

Sequential model-based optimization with a `scipy.optimize` interface

Distribution transparent Machine Learning experiments on Apache Spark