The TensorFlow Model Optimization Toolkit is a suite of tools for optimizing ML models for deployment and execution.

Computer Graphics Meets Deep Learning.

An open source framework for machine learning and other computations on decentralized data.

TensorFlow Probability is a library for probabilistic reasoning and statistical analysis.

Tensor2Tensor is a library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.

A Python library that includes implementations of TensorFlow optimizers for training machine learning models with differential privacy.

A library for reinforcement learning in TensorFlow.

A research framework for fast prototyping of reinforcement learning algorithms.

TRFL (pronounced “truffle”) is a library for reinforcement learning building blocks created by DeepMind.

A language for distributed deep learning, capable of specifying a broad class of distributed tensor computations.

Makes it easy to store and manipulate data with non-uniform shape, including text (words, sentences, characters), and batches of variable length.

Supports working with Unicode text directly in TensorFlow.

TensorFlow Ranking is a library for Learning-to-Rank (LTR) techniques on the TensorFlow platform.

Magenta is a research project exploring the role of machine learning in the process of creating art and music.

Nucleus is a library of Python and C++ code designed to make it easy to read, write and analyze data in common genomics file formats like SAM and VCF.

A library from DeepMind for constructing neural networks.

A learning framework to train neural networks by leveraging structured signals in addition to feature inputs.

Extra functionality for TensorFlow, maintained by SIG Addons.

Dataset, streaming, and file system extensions, maintained by SIG IO.