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.
Private AI platform for agents, assistants and enterprise search. Built-in Agent Builder, Deep research, Document analysis, Multi-model support, and API connectivity for agents.
A hyperparameter optimization framework
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
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
The AI Compute Platform for frontier teams. SkyPilot turns fragmented AI compute into one AI supercomputer, so frontier AI teams build custom intelligence faster.
StellarGraph - Machine Learning on Graphs
Algorithms for explaining machine learning models
Interpretability and explainability of data and machine learning models
moDel Agnostic Language for Exploration and eXplanation
[CONTRIBUTORS WELCOME] Generalized Additive Models in Python
A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.
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
Configure Python functions explicitly and safely
Contrastive Explanation (Foil Trees), developed at TNO/Utrecht University
German small and large versions of GPT2.