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 a desktop, server, or mobile or embedded device 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 when performing large-scale numerical computations on large data. Rajat Monga offers a high level introduction to TensorFlow and explains how to use it to train and deploy machine-learning models to make your next application smarter.
Rajat Monga leads TensorFlow, an open source machine learning library and the center of Google’s efforts at scaling up deep learning. He is one of the founding members of the Google Brain team and is interested in pushing machine learning research forward toward general AI. Previously, Rajat was the chief architect and director of engineering at Attributor, where he led the labs and operations and built out the engineering team. A veteran developer, Rajat has worked at eBay, Infosys, and a number of startups.
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