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PyTorch Deep Learning Hands-On

You're reading from   PyTorch Deep Learning Hands-On Build CNNs, RNNs, GANs, reinforcement learning, and more, quickly and easily

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Product type Paperback
Published in Apr 2019
Publisher Packt
ISBN-13 9781788834131
Length 250 pages
Edition 1st Edition
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Authors (2):
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Sherin Thomas Sherin Thomas
Author Profile Icon Sherin Thomas
Sherin Thomas
Sudhanshu Passi Sudhanshu Passi
Author Profile Icon Sudhanshu Passi
Sudhanshu Passi
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Toc

Chapter 5. Sequential Data Processing

The major challenges that neural networks are trying to solve today are processing, understanding, compressing, and generating sequential data. Sequential data can be described vaguely as anything that has a dependency on the previous data point and the next data point. Handling different types of sequential data requires different techniques, although the basic approach can be generalized. We'll explore what the basic building blocks of sequential data processing units are as well as, the common problems and their widely accepted solutions.

In this chapter, we are going to look at sequential data. The canonical data that people use for sequential data processing is natural language, although time series data, music, sound, and others are also considered to be sequential data. Natural language processing (NLP) and understanding has been explored extensively and it's an active field of research right now. The human language...

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