Swift 4 TensorFlow

In this talk, we look at the deliberation process that helped explain “Why Swift for TensorFlow?”. The choice was guided by the goals of the project, which imposed specific technical requirements which we cover in this talk.

Swift for TensorFlow features:

• First-class autodiff:
  Differentiable programming gets first-class support in a general-purpose programming language. Take derivatives of any function, or make custom data structures differentiable at your fingertips.

• Next-generation APIs:
  New APIs informed by the best practices of today, and the research directions of tomorrow, are both easier to use and more powerful.

• Builds on TensorFlow:
  Building on TensorFlow, the Swift APIs give you transparent access to all low-level TensorFlow operators.

• High-quality tooling:
  Building upon Jupyter and LLDB, Swift in Colab improves your productivity with helpful tooling such as context-aware autocomplete.

A cornerstone of the design is an algorithm that is called Graph Program Extraction, which allows you to write in an eager execution-style programming model while retaining all of the benefits of graphs. The design also includes support for advanced automatic differentiation built directly into Swift.

By the end of the talk, the audience will have a good overview of what is Machine Learning, how TensorFlow works and why Swift for TensorFlow is the best way to work with ML.