Review: TensorFlow shines a light on deep learning

Google's open source framework for machine learning and neural networks is fast and flexible, rich in models, and easy to run on CPUs or GPUs

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At a Glance

What makes Google Google? Arguably it is machine intelligence, along with a vast sea of data to apply it to. While you may never have as much data to process as Google does, you can use the very same machine learning and neural network library as Google. That library, TensorFlow, was developed by the Google Brain team over the past several years and released to open source in November 2015.

TensorFlow does computation using data flow graphs. Google uses TensorFlow internally for many of its products, both in its datacenters and on mobile devices. For example, the Translate, Maps, and Google apps all use TensorFlow-based neural networks running on our smartphones. And TensorFlow underpins the applied machine learning APIs for Google Cloud Natural Language, Speech, Translate, and Vision.

Data flow graphs are directed acyclic graphs that describe a computation for TensorFlow. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. This flexible architecture lets you deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device without rewriting code. In addition to the library, TensorFlow includes an interactive program for displaying data flow graphs, TensorBoard.

The principal language for using TensorFlow is Python, although there is limited support for C++. The tutorials supplied with TensorFlow include applications for handwritten digit classification, image recognition, word embeddings, recurrent neural networks, sequence-to-sequence models for machine translation, natural language processing, and PDE (partial differential equation)-based simulations.

tensorflow data flow

This is Google’s animated illustration of how tensors flow from node to node through the edges of a data flow graph.

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