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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

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Product type Paperback
Published in Nov 2017
Publisher Packt
ISBN-13 9781788390392
Length 274 pages
Edition 1st Edition
Languages
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Authors (2):
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Manpreet Singh Ghotra Manpreet Singh Ghotra
Author Profile Icon Manpreet Singh Ghotra
Manpreet Singh Ghotra
Rajdeep Dua Rajdeep Dua
Author Profile Icon Rajdeep Dua
Rajdeep Dua
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Toc

Table of Contents (11) Chapters Close

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

Summary

In this chapter, you learned three different types of autoencoders: basic, Additive Gaussian Noise, and Sparse. We understood the use cases where they can be useful. We ran them against the MNIST dataset and also compared the cost of the three autoencoders. We also plotted the weights as well as the approximate output.

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