Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
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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 hyperparameter optimization framework
The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.
Fast and Accurate ML in 3 Lines of Code
Automated Machine Learning with scikit-learn
Keras + Hyperopt: A very simple wrapper for convenient hyperparameter optimization
Hyperparameter Experiments with TensorFlow and Keras
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
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.
Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research.
Hyperparameter optimization and feature selection for scikit-learn using evolutionary algorithms. A modern alternative to GridSearchCV and RandomizedSearchCV.
Bayesian Optimization using GPflow
Auto-optimizing a neural net (and its architecture) on the CIFAR-100 dataset. Could be easily transferred to another dataset or another classification task.