Machine learning tools are so important that they deserve a first-class language and a compiler. Swift for TensorFlow combines the flexibility of eager execution with the high performance of graphs and sessions. Behind the scenes, Swift analyzes your Tensor code and automatically builds graphs for you. It also catches type errors and shape mismatches before running your code, has the ability to import any Python library, and has language-integrated automatic differentiation.
TensorFlow Lite is a lightweight machine learning framework that can do inference on a variety of mobile and small devices, from mobile phones to Raspberry Pis and microcontrollers. It also provides a simple abstraction that allows you to access AI accelerators.
Richard Wei and Andrew Selle discuss both frameworks, covering the current status of development and the latest developments. You’ll learn how to prepare your model for mobile and how to write code that executes it on a variety of different platforms.
This session is sponsored by Google.
Richard Wei is a software engineer on the Google Brain team at Google, working on APIs and automatic differentiation for Swift for TensorFlow. Previously, he worked on Siri at Apple. Richard studied computer science and linguistics at the University of Illinois at Urbana-Champaign, where he developed compiler technologies for machine learning.
Andrew Selle is a senior staff software engineer for TensorFlow Lite at Google and is one of its initial architects. He’s also worked on improvements to the core and API of TensorFlow. Previously, he worked extensively in research and development of highly parallel numerical physical simulation techniques for physical phenomena for film and physically based rendering. He worked on several Walt Disney Animation Films including Frozen and Zootopia. He holds a PhD in computer science from Stanford University.
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