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You're reading from Hands-On Natural Language Processing with Python A practical guide to applying deep learning architectures to your NLP applications

Product type Paperback

Published in Jul 2018

Publisher Packt

ISBN-13 9781789139495

Length 312 pages

Edition 1st Edition

Languages

Processing

Tools

NLTK

Concepts

Deep Learning

Authors (5):

Rajalingappaa Shanmugamani

Chaitanya Joshi

Auguste Byiringiro

Rajesh Arumugam

Karthik Muthuswamy

+1 more

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Table of Contents (15) Chapters

Preface

1. Getting Started FREE CHAPTER

2. Text Classification and POS Tagging Using NLTK

3. Deep Learning and TensorFlow

4. Semantic Embedding Using Shallow Models

5. Text Classification Using LSTM

6. Searching and DeDuplicating Using CNNs

7. Named Entity Recognition Using Character LSTM

8. Text Generation and Summarization Using GRUs

9. Question-Answering and Chatbots Using Memory Networks

10. Machine Translation Using the Attention-Based Model

11. Speech Recognition Using DeepSpeech

12. Text-to-Speech Using Tacotron

13. Deploying Trained Models

14. Other Books You May Enjoy

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Summary

In this chapter, we saw how to use character-based CNN, by training a model to search for duplicate pairs. Character CNN gives us the flexibility to train models with unknown characters, and it is more generic than word-level embedding. Similar kinds of networks can be used for searching, matching, and deduplication.

In the next chapter, we will learn how to train a model for Named Entity Recognition (NER) using LSTM.

The rest of the chapter is locked

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Authors (5)

Shanmugamani

Rajalingappaa Shanmugamani is currently working as an Engineering Manager for a Deep learning team at Kairos. Previously, he worked as a Senior Machine Learning Developer at SAP, Singapore and worked at various startups in developing machine learning products. He has a Masters from Indian Institute of TechnologyMadras. He has published articles in peer-reviewed journals and conferences and submitted applications for several patents in the area of machine learning. In his spare time, he coaches programming and machine learning to school students and engineers.

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Muthuswamy

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Byiringiro

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Arumugam

Rajesh Arumugam is an ML developer at SAP, Singapore. Previously, he developed ML solutions for smart city development in areas such as passenger flow analysis in public transit systems and optimization of energy consumption in buildings when working with Centre for Social Innovation at Hitachi Asia, Singapore. He has published papers in conferences and has pending patents in storage and ML. He holds a PhD in computer engineering from Nanyang Technological University, Singapore.

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Joshi

Vijay Joshi is a full stack web developer having more than a decade of experience in working with PHP and JavaScript.

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