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Python Natural Language Processing

You're reading from   Python Natural Language Processing Advanced machine learning and deep learning techniques for natural language processing

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781787121423
Length 486 pages
Edition 1st Edition
Languages
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Author (1):
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Jalaj Thanaki Jalaj Thanaki
Author Profile Icon Jalaj Thanaki
Jalaj Thanaki
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Toc

Table of Contents (13) Chapters Close

Preface 1. Introduction FREE CHAPTER 2. Practical Understanding of a Corpus and Dataset 3. Understanding the Structure of a Sentences 4. Preprocessing 5. Feature Engineering and NLP Algorithms 6. Advanced Feature Engineering and NLP Algorithms 7. Rule-Based System for NLP 8. Machine Learning for NLP Problems 9. Deep Learning for NLU and NLG Problems 10. Advanced Tools 11. How to Improve Your NLP Skills 12. Installation Guide

Deep learning and deep neural networks

Now, just shift from ANN to DNN. In the upcoming section, we will see deep learning, architecture of DNN, and compare the approaches of DL for NLP and ML for NLP.

Revisiting DL

We have seen some basic details about DL. Here, the purpose is just to recall things as a little refresher. ANN that is not two or three layers but many layers deep is called DNN. When we use many layers deep neural networks on lots of data using lots of computing power, we call this process deep learning.

Let's see the architecture of a deep neural network.

The basic architecture of DNN

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