Repository Index

Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

• ray
• distributed
• parallel
• machine-learning

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

• deep-learning
• machine-learning
• book
• notebook

A hyperparameter optimization framework

• python
• machine-learning
• parallel
• distributed

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

• machine-learning
• experiment-track
• deep-learning
• keras

Fast and Accurate ML in 3 Lines of Code

• automl
• machine-learning
• data-science
• deep-learning

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

• machine-learning
• python
• data-science
• automl

Automated Machine Learning with scikit-learn

• automl
• scikit-learn
• automated-machine-learning
• hyperparameter-optimization

AI Infra / AI Orchestration / AI Control Plane

• deep-learning
• machine-learning
• artificial-intelligence
• data-science

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

• deep-learning
• machine-learning
• ml-platform
• ml-infrastructure

Keras + Hyperopt: A very simple wrapper for convenient hyperparameter optimization

• hyperopt
• keras
• hyperparameter-optimization

Hyperparameter Experiments with TensorFlow and Keras

• deep-learning
• keras
• keras-tensorflow
• hyperparameter-optimization

SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization

• bayesian-optimization
• bayesian-optimisation
• hyperparameter-optimization
• hyperparameter-tuning

The world's cleanest AutoML library ✨ - Do hyperparameter tuning with the right pipeline abstractions to write clean deep learning production pipelines. Let your pipeline steps have hyperparameter spaces. Design steps in your pipeline like components. Compatible with Scikit-Learn, TensorFlow, and most other libraries, frameworks and MLOps environments.

• pipeline
• pipeline-framework
• machine-learning
• deep-learning

Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research.

• python
• pytorch
• machine-learning
• deep-learning

Hyperparameter optimization and feature selection for scikit-learn using evolutionary algorithms. A modern alternative to GridSearchCV and RandomizedSearchCV.

• python
• scikit-learn
• machine-learning
• artificial-intelligence

Bayesian Optimization using GPflow

• gpflow
• bayesian-optimization
• machine-learning
• hyperparameter-optimization

Auto-optimizing a neural net (and its architecture) on the CIFAR-100 dataset. Could be easily transferred to another dataset or another classification task.

• hyperopt
• hyperparameter-optimization
• hyperparameter-tuning
• hyperparameters-optimization