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Deep Learning with TensorFlow

You're reading from   Deep Learning with TensorFlow Explore neural networks and build intelligent systems with Python

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Product type Paperback
Published in Mar 2018
Publisher Packt
ISBN-13 9781788831109
Length 484 pages
Edition 2nd Edition
Languages
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Authors (2):
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Giancarlo Zaccone Giancarlo Zaccone
Author Profile Icon Giancarlo Zaccone
Giancarlo Zaccone
Md. Rezaul Karim Md. Rezaul Karim
Author Profile Icon Md. Rezaul Karim
Md. Rezaul Karim
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Table of Contents (13) Chapters Close

Preface 1. Getting Started with Deep Learning 2. A First Look at TensorFlow FREE CHAPTER 3. Feed-Forward Neural Networks with TensorFlow 4. Convolutional Neural Networks 5. Optimizing TensorFlow Autoencoders 6. Recurrent Neural Networks 7. Heterogeneous and Distributed Computing 8. Advanced TensorFlow Programming 9. Recommendation Systems Using Factorization Machines 10. Reinforcement Learning Other Books You May Enjoy Index

Chapter 7. Heterogeneous and Distributed Computing

A computation expressed using TensorFlow can be executed with little or no changes on a wide variety of heterogeneous systems, ranging from mobile devices such as phones and tablets up to large-scale distributed systems of hundreds of machines and thousands of computational devices, such as GPU cards.

In this chapter, we explore this fundamental topic on TensorFlow. In particular, we shall consider the possibility of executing TensorFlow models on GPU cards and distributed systems.

GPUs have additional advantages over CPUs, including having more computational units and having a higher bandwidth for memory retrieval. Furthermore, in many deep learning applications that require a lot of computational effort, GPU graphics specific capabilities can be exploited to further speed up calculations.

At the same time, a distributed computing strategy can be useful if you have to handle a very large dataset to train your model.

The chapter introduces...

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