You're reading from Deep Learning for Natural Language Processing Solve your natural language processing problems with smart deep neural networks

Product type Paperback

Published in Jun 2019

Publisher

ISBN-13 9781838550295

Length 372 pages

Edition 1st Edition

Languages

Processing

Tools

Processing

Concepts

Deep Learning

Authors (4):

Karthiek Reddy Bokka

Monicah Wambugu

Tanuj Jain

Shubhangi Hora

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Table of Contents (11) Chapters

About the Book

About the Authors
Description
Learning Objectives
Audience
Approach
Hardware Requirements
Software Requirements
Conventions
Installation and Setup
Install Python on Windows
Install Python on Linux
Install Python on macOS X
Installing Keras
Additional Resources

1. Introduction to Natural Language Processing FREE CHAPTER

Introduction
The Basics of Natural Language Processing
Capabilities of Natural language processing
Applications of Natural Language Processing
Word Embeddings
Summary

2. Applications of Natural Language Processing

Introduction
POS Tagging
Applications of Parts of Speech Tagging
Chunking
Chinking
Named Entity Recognition
Summary

3. Introduction to Neural Networks

Introduction
Neural Networks
Training a Neural Network
Designing a Neural Network and Its Applications
Fundamentals of Deploying a Model as a Service
Summary

4. Foundations of Convolutional Neural Network

Introduction
Understanding the Architecture of a CNN
Training a CNN
Application Areas of CNNs
Summary

5. Recurrent Neural Networks

Introduction
Previous Versions of Neural Networks
RNNs
Updates and Gradient Flow
Gradients
Summary

6. Gated Recurrent Units (GRUs)

Introduction
The Drawback of Simple RNNs
Gated Recurrent Units (GRUs)
Sentiment Analysis with GRU
Summary

7. Long Short-Term Memory (LSTM)

Introduction
The Input Gate and the Candidate Cell State
Output Gate and Current Activation
Neural Language Translation
Summary

8. State-of-the-Art Natural Language Processing

Introduction
Other Architectures and Developments
Activity 11: Build a Text Summarization Model
Summary

9. A Practical NLP Project Workflow in an Organization

Introduction
Problem Definition
Data Acquisition
Google Colab
Flask
Deployment
Summary

1. Appendix

Chapter 1: Introduction to Natural Language Processing
Chapter 2: Applications of Natural Language Processing
Chapter 3: Introduction to Neural Networks
Chapter 4: Introduction to convolutional networks
Chapter 5: Foundations of Recurrent Neural Network
Chapter 6: Foundations of GRUs
Chapter 7: Foundations of LSTM
Chapter 8: State of the art in Natural Language Processing
Chapter 9: A practical NLP project workflow in an organisation

Appendix

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You're reading from Deep Learning for Natural Language Processing Solve your natural language processing problems with smart deep neural networks

Table of Contents (11) Chapters

Appendix

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Authors (4)

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