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Deep Learning with R Cookbook

You're reading from   Deep Learning with R Cookbook Over 45 unique recipes to delve into neural network techniques using R 3.5.x

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Product type Paperback
Published in Feb 2020
Publisher Packt
ISBN-13 9781789805673
Length 328 pages
Edition 1st Edition
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Authors (3):
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Swarna Gupta Swarna Gupta
Author Profile Icon Swarna Gupta
Swarna Gupta
Rehan Ali Ansari Rehan Ali Ansari
Author Profile Icon Rehan Ali Ansari
Rehan Ali Ansari
Dipayan Sarkar Dipayan Sarkar
Author Profile Icon Dipayan Sarkar
Dipayan Sarkar
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Table of Contents (11) Chapters Close

Preface 1. Understanding Neural Networks and Deep Neural Networks 2. Working with Convolutional Neural Networks FREE CHAPTER 3. Recurrent Neural Networks in Action 4. Implementing Autoencoders with Keras 5. Deep Generative Models 6. Handling Big Data Using Large-Scale Deep Learning 7. Working with Text and Audio for NLP 8. Deep Learning for Computer Vision 9. Implementing Reinforcement Learning 10. Other Books You May Enjoy

Recurrent Neural Networks in Action

Sequence data is data where the order matters, such as in audio, video, and speech. Learning sequential data is one of the most challenging problems in the field of pattern recognition because of the nature of the data. The dependencies between the parts of sequences and their varying length add further complexity when processing sequential data. With the advent of sequence models and algorithms such as recurrent neural networks (RNN), long short-term memory models (LSTM), and gated recurrent units (GRU), sequence data modeling is being utilized in multiple applications, such as sequence classification, sequence generation, speech to text conversion, and many more.

In sequence classification, the goal is to predict the category of the sequence, whereas in sequence generation, we generate a new output sequence based on...

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