TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in desktop, server, mobile or embedded devices with a single API. Originally developed by researchers and engineers at Google for the purposes of conducting machine-learning and deep neural networks research, TensorFlow leverages a general computational model that is applicable in a wide variety of other domains, especially for performing large-scale numerical computations on large data. Sherry Moore offers a high-level introduction to TensorFlow and explains how to use it to train machine-learning models to make your next application smarter.
Sherry Moore is a software engineer on the Google Brain team. Her other projects at Google include Google Fiber and Google Ads Extractor. Previously, she spent 14 years as a systems and kernel engineer at Sun Microsystems.
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