TensorFlow is an open source software library from Google for numerical computation using data flow graphs. It provides a flexible platform for defining and running machine-learning algorithms and is particularly suited for neural net applications. Julia Ferraioli, Amy Unruh, and Eli Bixby demonstrate how to use TensorFlow to define, train, and utilize a variety of machine-learning algorithms on a number of datasets.
Julia, Amy, and Eli start by providing some background and motivation for problems in machine learning, as well as a brief history of the field, from the perspective of both Google and the machine-learning community as a whole. They then give a brief overview of how Google uses TensorFlow in its services before diving into an in-depth, hands-on exploration of TensorFlow.
Julia Ferraioli is a Senior Developer Advocate with Google’s Open Source Programs Office. She’s a polyglot, though in code only, and is excited about open source sustainability, accessibility, machine learning, containers, and sprinkles (in roughly that order). Her superpowers are finding ways to incorporate her interests into her work and estimating how much stuff can fit inside a container.
Amy Unruh is a developer programs engineer at Google for the Google Cloud Platform, where she works with TensorFlow as well as many other Cloud Platform technologies. Amy has a PhD in CS/AI, has worked in academia, at several startups, and in industrial R&D, and has published a book on App Engine.
Eli Bixby is a developer programs engineer at Google currently developing on Google Cloud Platform’s DevOps distributed systems, machine-learning, and big data offerings. He joined Google as a developer programs engineer. Previously, Eli dabbled in several research areas, with papers in biophysics, algorithmic game theory, and most recently computational biology.
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