Course
Swift for TensorFlow
Swift for TensorFlow combines the flexibility of Eager Execution with the high performance of graphs and sessions. Swift can analyze your Tensor code in the background and automatically build graphs. It also captures type errors and shape mismatches before running the code, allows input from any Python library, and features language-integrated automatic differentiation. We believe that machine learning tools are extremely important, and thus it is necessary to adopt top-notch languages and compilers.
TensorFlow Lite
TensorFlow Lite is a lightweight machine learning framework that enables inference on a variety of mobile and small devices (from smartphones to Raspberry Pis and microcontrollers). It also provides a simple abstraction that allows users to access AI accelerators. The team introduced the basics of this framework, its current development status, and the latest advancements. In this course, you will learn how to prepare models suitable for mobile devices and how to write code that can run on various different platforms.
TensorFlow Extended (TFX) and Hub
In this lecture, the team introduces TensorFlow Extended (TFX), an end-to-end TensorFlow machine learning platform that supports all of Alphabet’s products. As machine learning evolves from experimentation to production workloads, the need for efficient management of end-to-end training and production workflows (including model management and version control) increases.
