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Deep Learning with TensorFlow

You're reading from  Deep Learning with TensorFlow

Product type Book
Published in Apr 2017
Publisher Packt
ISBN-13 9781786469786
Pages 320 pages
Edition 1st Edition
Languages
Authors (3):
Giancarlo Zaccone Giancarlo Zaccone
Profile icon Giancarlo Zaccone
Md. Rezaul Karim Md. Rezaul Karim
Profile icon Md. Rezaul Karim
Ahmed Menshawy Ahmed Menshawy
Profile icon Ahmed Menshawy
View More author details

Table of Contents (11) Chapters

Preface 1. Getting Started with Deep Learning 2. First Look at TensorFlow 3. Using TensorFlow on a Feed-Forward Neural Network 4. TensorFlow on a Convolutional Neural Network 5. Optimizing TensorFlow Autoencoders 6. Recurrent Neural Networks 7. GPU Computing 8. Advanced TensorFlow Programming 9. Advanced Multimedia Programming with TensorFlow 10. Reinforcement Learning

Summary

TensorFlow is designed to make distributed machine and deep learning easy for everyone, but using it does require understanding some general principles and algorithms. Furthermore, the latest release of TensorFlow comes with lots of exciting features. Thus we also tried to cover them so that you can use them with ease. We have shown how to install TensorFlow on different platforms including Linux, Windows, and Mac OS. Before, covering this in even greater depth, we showed some example of how to upgrade the source code from the previous version of TensorFlow to the latest version 1.x.

In summary, here is a brief recap of the key concepts of TensorFlow explained in this chapter:

  • Graph: A TensorFlow computation is represented as dataflow graph. Each graph is built as a set of operations (http://www.tensorflow.org/api_docs/python/framework.html#Operation) objects.
  • Operation: Each Operation object represents...
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