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Deep Learning with TensorFlow

You're reading from   Deep Learning with TensorFlow Explore neural networks with Python

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Product type Paperback
Published in Apr 2017
Publisher Packt
ISBN-13 9781786469786
Length 320 pages
Edition 1st Edition
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Authors (4):
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Md. Rezaul Karim Md. Rezaul Karim
Author Profile Icon Md. Rezaul Karim
Md. Rezaul Karim
Ahmed Menshawy Ahmed Menshawy
Author Profile Icon Ahmed Menshawy
Ahmed Menshawy
Giancarlo Zaccone Giancarlo Zaccone
Author Profile Icon Giancarlo Zaccone
Giancarlo Zaccone
Fabrizio Milo Fabrizio Milo
Author Profile Icon Fabrizio Milo
Fabrizio Milo
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Toc

Table of Contents (11) Chapters Close

Preface 1. Getting Started with Deep Learning FREE CHAPTER 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

GPUs are specialized hardware components that were originally developed for graphics applications. However, it was realized that GPUs could be used to perform computations in a DNN architecture.

In the second part of this chapter, we saw how to install a GPU-enabled TensorFlow and how to manage GPU devices.

In the next chapter, we will discuss some advanced programming features using TensorFlow by integrating with other third-party tools such as, Keras, PrettyTensor and TFLearn. In addition, we’ll give an overview of some TensorFlow-based framework--Keras, PrettyTensor and TFLearn.

We'll show the main topics of these frameworks, with very interesting application examples.

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