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Deep Learning with TensorFlow

You're reading from  Deep Learning with TensorFlow

Product type Book
Published in Apr 2017
Publisher Packt
ISBN-13 9781786469786
Pages 320 pages
Edition 1st Edition
Languages
Authors (3):
Giancarlo Zaccone Giancarlo Zaccone
Profile icon Giancarlo Zaccone
Md. Rezaul Karim Md. Rezaul Karim
Profile icon Md. Rezaul Karim
Ahmed Menshawy Ahmed Menshawy
Profile icon Ahmed Menshawy
View More author details

Table of Contents (11) Chapters

Preface 1. Getting Started with Deep Learning 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

Autoencoders

Stacked autoencoders are DNNs that are typically used for data compression. Their particular hourglass structure clearly shows the first part of the process, where the input data is compressed, up to the so-called bottleneck, from which the decompression starts.

The output is then an approximation of the input. These networks are not supervised in the pretraining (compression) phase, and the fine-tuning (decompression) phase is supervised:

Figure 11: Stack autoencoder architecture
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Deep Learning with TensorFlow
Published in: Apr 2017 Publisher: Packt ISBN-13: 9781786469786
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