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You're reading from  TensorFlow 2.0 Quick Start Guide

Product typeBook
Published inMar 2019
Reading LevelBeginner
PublisherPackt
ISBN-139781789530759
Edition1st Edition
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Author (1)
Tony Holdroyd
Tony Holdroyd
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Tony Holdroyd

Tony Holdroyd's first degree, from Durham University, was in maths and physics. He also has technical qualifications, including MCSD, MCSD.net, and SCJP. He holds an MSc in computer science from London University. He was a senior lecturer in computer science and maths in further education, designing and delivering programming courses in many languages, including C, C+, Java, C#, and SQL. His passion for neural networks stems from research he did for his MSc thesis. He has developed numerous machine learning, neural network, and deep learning applications, and has advised in the media industry on deep learning as applied to image and music processing. Tony lives in Gravesend, Kent, UK, with his wife, Sue McCreeth, who is a renowned musician.
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Looking at the modern TensorFlow ecosystem

Let's discuss eager execution. The first incarnation of TensorFlow involved constructing a computational graph made up of operations and tensors, which had to be subsequently evaluated in what Google termed as session (this is known as declarative programming). This is still a common way to write TensorFlow programs. However, eager execution, available from release 1.5 onward in research form and baked into TensorFlow proper from release 1.7, involves the immediate evaluation of operations, with the consequence that tensors can be treated like NumPy arrays (this is known as imperative programming).

Google says that eager execution is the preferred method for research and development but that computational graphs are to be preferred for serving TensorFlow production applications.

tf.data is an API that allows you to build complicated...

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TensorFlow 2.0 Quick Start Guide
Published in: Mar 2019Publisher: PacktISBN-13: 9781789530759

Author (1)

author image
Tony Holdroyd

Tony Holdroyd's first degree, from Durham University, was in maths and physics. He also has technical qualifications, including MCSD, MCSD.net, and SCJP. He holds an MSc in computer science from London University. He was a senior lecturer in computer science and maths in further education, designing and delivering programming courses in many languages, including C, C+, Java, C#, and SQL. His passion for neural networks stems from research he did for his MSc thesis. He has developed numerous machine learning, neural network, and deep learning applications, and has advised in the media industry on deep learning as applied to image and music processing. Tony lives in Gravesend, Kent, UK, with his wife, Sue McCreeth, who is a renowned musician.
Read more about Tony Holdroyd