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Deep Learning with TensorFlow. - Second Edition

You're reading from Deep Learning with TensorFlow. - Second Edition

Product type Book

Published in Mar 2018

Publisher Packt

ISBN-13 9781788831109

Pages 484 pages

Edition 2nd Edition

Languages

Concepts

Authors (2):

Giancarlo Zaccone

Md. Rezaul Karim

View More author details

Table of Contents (15) Chapters

Deep Learning with TensorFlow - Second Edition

Contributors

Preface

Other Books You May Enjoy

1. Getting Started with Deep Learning

2. A First Look at TensorFlow

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

Index

How does an autoencoder work?

Autoencoding is a data compression technique where the compression and decompression functions are data-specific, lossy, and learned automatically from samples rather than human-crafted manual features. Additionally, in almost all contexts where the term autoencoder is used, the compression and decompression functions are implemented with NNs.

An autoencoder is a network with three or more layers, where the input and the output layers have the same number of neurons, and those intermediate (hidden layers) have a lower number of neurons. The network is trained to reproduce output simply, for each piece of input data, the same pattern of activity in the input.

The remarkable aspect of autoencoders is that, due to the lower number of neurons in the hidden layer, if the network can learn from examples and generalize to an acceptable extent, it performs data compression: the status of the hidden neurons provides, for each example, a compressed version of the input...

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Authors (2)

Giancarlo Zaccone

Giancarlo Zaccone has over fifteen years' experience of managing research projects in the scientific and industrial domains. He is a software and systems engineer at the European Space Agency (ESTEC), where he mainly deals with the cybersecurity of satellite navigation systems. Giancarlo holds a master's degree in physics and an advanced master's degree in scientific computing. Giancarlo has already authored the following titles, available from Packt: Python Parallel Programming Cookbook (First Edition), Getting Started with TensorFlow, Deep Learning with TensorFlow (First Edition), and Deep Learning with TensorFlow (Second Edition).

See other products by Giancarlo Zaccone

Md. Rezaul Karim

Md. Rezaul Karim is a researcher, author, and data science enthusiast with a strong computer science background, coupled with 10 years of research and development experience in machine learning, deep learning, and data mining algorithms to solve emerging bioinformatics research problems by making them explainable. He is passionate about applied machine learning, knowledge graphs, and explainable artificial intelligence (XAI). Currently, he is working as a research scientist at Fraunhofer FIT, Germany. He is also a PhD candidate at RWTH Aachen University, Germany. Before joining FIT, he worked as a researcher at the Insight Centre for Data Analytics, Ireland. Previously, he worked as a lead software engineer at Samsung Electronics, Korea.

See other products by Md. Rezaul Karim

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