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Deep Learning with TensorFlow

You're reading from   Deep Learning with TensorFlow Explore neural networks and build intelligent systems with Python

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Product type Paperback
Published in Mar 2018
Publisher Packt
ISBN-13 9781788831109
Length 484 pages
Edition 2nd Edition
Languages
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Authors (2):
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Giancarlo Zaccone Giancarlo Zaccone
Author Profile Icon Giancarlo Zaccone
Giancarlo Zaccone
Md. Rezaul Karim Md. Rezaul Karim
Author Profile Icon Md. Rezaul Karim
Md. Rezaul Karim
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Table of Contents (13) Chapters Close

Preface 1. Getting Started with Deep Learning 2. A First Look at TensorFlow FREE CHAPTER 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 Other Books You May Enjoy Index

Chapter 6. Recurrent Neural Networks

A RNN is a class of ANN where connections between units form a directed cycle. RNNs make use of information from the past. That way, they can make predictions in data with high temporal dependencies. This creates an internal state of the network, which allows it to exhibit dynamic temporal behavior. In this chapter, we will develop several real-life predictive models, using RNNs and their architectural variants, to make predictive analytics easier.

First, we will provide some theoretical background of RNNs. Then we will look at a few examples that will show a systematic way of implementing predictive models for image classification, sentiment analysis of movies, and spam predictions for Natural Language Processing (NLP).

Then we will show how to develop predictive models for time series data. Finally, we will see a how to develop a LSTM network for solving more advanced problems, such as human activity recognition.

Concisely, the following topics...

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