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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

Summary

Congratulations guys! We have made it to the last chapter! I really appreciate your efforts. In this chapter, you have learned a lot of things such as artificial intelligence aspects that help you understand why deep learning is the buzzword nowadays. We have seen the concept of ANNs. We have seen concepts such as gradient descent, various activation functions, and loss functions. We have seen the architecture of DNN and the DL life cycle. We have also touched on the basics of the sequence-to-sequence model and developed applications such as machine translation, title generation, and summarization. We have also seen the gradient descent-based optimization techniques.

The next sections are Appendices A to C, that will provide you with an overview about frameworks such as hadoop, spark, and so on. You can also see the installation guide for these frameworks as well as other...

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