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Neural Network Programming with TensorFlow

You're reading from   Neural Network Programming with TensorFlow Unleash the power of TensorFlow to train efficient neural networks

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Product type Paperback
Published in Nov 2017
Publisher Packt
ISBN-13 9781788390392
Length 274 pages
Edition 1st Edition
Languages
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Authors (2):
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Manpreet Singh Ghotra Manpreet Singh Ghotra
Author Profile Icon Manpreet Singh Ghotra
Manpreet Singh Ghotra
Rajdeep Dua Rajdeep Dua
Author Profile Icon Rajdeep Dua
Rajdeep Dua
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Toc

Table of Contents (11) Chapters Close

Preface 1. Maths for Neural Networks FREE CHAPTER 2. Deep Feedforward Networks 3. Optimization for Neural Networks 4. Convolutional Neural Networks 5. Recurrent Neural Networks 6. Generative Models 7. Deep Belief Networking 8. Autoencoders 9. Research in Neural Networks 10. Getting started with TensorFlow

Computational graph

TensorFlow is based on building a computational graph. A computational graph is a network of nodes, where each node defines an operation running a function; this can be as plain as addition or subtraction, or as complicated as a multivariate equation. TensorFlow programs are structured in a construction phase that assembles a graph and an execution phase that utilizes a session object to execute operations in the graph.

An operation is referred to as the op and can return zero or more tensors, which can be used later in the graph. Each op can be given a constant, array, or n-dimensional matrix. 

Graph

The default graph gets instantiated when the TensorFlow library is imported. Constructing a graph...

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