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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 2. First Look at TensorFlow FREE CHAPTER 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

In this chapter, we have implemented some optimizing networks, called autoencoders. An autoencoder is basically a data-compression network model.

It is used to encode a given input into a representation of a smaller dimension; then, a decoder can be used to reconstruct the input back from the encoded version. All the autoencoders we implemented contain an encoding, and a decoding, part.

We have also looked at how to improve the autoencoder's performance, introducing a noise during network training, and building a denoising autoencoder. Finally, we applied the concepts of the CNN networks introduced in Chapter 4, TensorFlow on a Convolutional Neural Network, with the implementation of convolutional autoencoders.

In the next chapter, we'll examine Recurrent Neural Networks (RNNs). We will start by describing the basic principles of these networks, and then we'll implement some interesting example...

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