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

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.

A curated list of automated machine learning papers, articles, tutorials, slides and projects

🔥 A tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.

Implementation of a comparative analysis to find the best ML model for classifying dry eye disease from healthy controls using metabolomics datasets.

Propulate is an asynchronous evolutionary optimization algorithm and software package for global optimization and hyperparameter search on high-performance computers.

Fast and Accurate ML in 3 Lines of Code

A hyperparameter optimization framework

Neural Pipeline Search (NePS): Helps deep learning experts find the best neural pipeline.

Examples for https://github.com/optuna/optuna

Open Source version of SigOpt API, performing hyperparameter optimization and visualization

Tutorials for the optimization techniques used in Gradient-Free-Optimizers and Hyperactive.

Python-based research interface for blackbox and hyperparameter optimization, based on the internal Google Vizier Service.

A hyperparameter optimization library for the ABCI.

This project aims to predict the income bracket of individuals based on a variety of features, and presents a holistic comparative analysis between multiple machine learning algorithms through hyperparameter optimization on a binary classification problem.

End-to-end ML project for tabular data.

Proyecto de investigación en ML para identificar factores genéticos en pronóstico de lesiones pre-tumorales. Aprendizaje no supervisado para discernir perfiles genéticos distintivos entre grupos de buen y mal pronóstico, mejorando detección y tratamiento temprano del cáncer.

Automated modeling and machine learning framework FEDOT

Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.

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