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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

Product type Paperback

Published in Nov 2017

Publisher Packt

ISBN-13 9781788390392

Length 274 pages

Edition 1st Edition

Languages

Python

Tools

TensorFlow

Concepts

Neural Networks

Authors (2):

Manpreet Singh Ghotra

Rajdeep Dua

View More author details

Table of Contents (11) Chapters

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 object instead of using the default graph is useful when creating multiple models in one file that do not depend on each other. Constants and operations are added to the graph in TensorFlow.

Variables and operations applied outside of newGraph.as_default() will get added to the...

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Authors (2)

Dua

Dua

Rajdeep Dua has over 18 years experience in the cloud and big data space. He has taught Spark and big data at some of the most prestigious tech schools in India: IIIT Hyderabad, ISB, IIIT Delhi, and Pune College of Engineering. He currently leads the developer relations team at Salesforce India. He has also presented BigQuery and Google App Engine at the W3C conference in Hyderabad. He led the developer relations teams at Google, VMware, and Microsoft, and has spoken at hundreds of other conferences on the cloud. Some of the other references to his work can be seen at Your Story and on ACM digital library. His contributions to the open source community relate to Docker, Kubernetes, Android, OpenStack, and Cloud Foundry.

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Ghotra

Ghotra

Manpreet Singh Ghotra has more than 15 years experience in software development for both enterprise and big data software. He is currently working at Salesforce on developing a machine learning platform/APIs using open source libraries and frameworks such as Keras, Apache Spark, and TensorFlow. He has worked on various machine learning systems, including sentiment analysis, spam detection, and anomaly detection. He was part of the machine learning group at one of the largest online retailers in the world, working on transit time calculations using Apache Mahout, and the R recommendation system, again using Apache Mahout. With a master's and postgraduate degree in machine learning, he has contributed to, and worked for, the machine learning community.

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