Warning: This project is deprecated. Swift for TensorFlow was an experiment in the
next-generation platform for machine learning, incorporating the latest research across
machine learning, compilers, differentiable programming, systems design, and beyond. It was
archived in February 2021.
Swift for TensorFlow
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Welcome to the Swift for TensorFlow API documentation.
Swift for TensorFlow is a next-generation platform for machine learning,
incorporating the latest research across machine learning, compilers,
differentiable programming, systems design, and beyond. This is an early-stage
project: it is not feature-complete nor production-ready, but it is ready for
pioneers to try in projects, give feedback, and help shape the future!
The Swift for TensorFlow project is currently focusing on 2 kinds of users:
Advanced ML researchers who are limited by current ML frameworks. Swift
for TensorFlow's advantages include seamless integration with a modern
general-purpose language, allowing for more dynamic and sophisticated
models. Fast abstractions can be developed in "user-space" (as opposed to in
C/C++, aka "framework-space"), resulting in modular APIs that can be easily
customized.
ML learners who are just getting started with machine learning. Thanks
to Swift's support for quality tooling (e.g. context-aware autocompletion),
Swift for TensorFlow can be one of the most productive ways to start
learning the fundamentals of machine learning.
For more information about Swift for TensorFlow (including installation and
usage instructions), please visit the
documentation repository.
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.
Last updated 2019-10-01 UTC.
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