9781838550295-1838550291-Deep Learning for Natural Language Processing: Solve your natural language processing problems with smart deep neural networks

Deep Learning for Natural Language Processing: Solve your natural language processing problems with smart deep neural networks

ISBN-13: 9781838550295
ISBN-10: 1838550291
Author: Reddy Bokka, Karthiek, Hora, Shubhangi, Jain, Tanuj, Wambugu, Monicah
Publication date: 2019
Publisher: Packt Publishing
Format: Paperback 372 pages
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Book details

ISBN-13: 9781838550295
ISBN-10: 1838550291
Author: Reddy Bokka, Karthiek, Hora, Shubhangi, Jain, Tanuj, Wambugu, Monicah
Publication date: 2019
Publisher: Packt Publishing
Format: Paperback 372 pages

Summary

Acknowledged authors Reddy Bokka, Karthiek, Hora, Shubhangi, Jain, Tanuj, Wambugu, Monicah wrote Deep Learning for Natural Language Processing: Solve your natural language processing problems with smart deep neural networks comprising 372 pages back in 2019. Textbook and eTextbook are published under ISBN 1838550291 and 9781838550295. Since then Deep Learning for Natural Language Processing: Solve your natural language processing problems with smart deep neural networks textbook was available to sell back to BooksRun online for the top buyback price or rent at the marketplace.

Description

Gain the knowledge of various deep neural network architectures and their application areas to conquer your NLP issues.

Key Features
  • Gain insights into the basic building blocks of natural language processing
  • Learn how to select the best deep neural network to solve your NLP problems
  • Explore convolutional and recurrent neural networks and long short-term memory networks
Book Description

Applying deep learning approaches to various NLP tasks can take your computational algorithms to a completely new level in terms of speed and accuracy. Deep Learning for Natural Language Processing starts off by highlighting the basic building blocks of the natural language processing domain. The book goes on to introduce the problems that you can solve using state-of-the-art neural network models. After this, delving into the various neural network architectures and their specific areas of application will help you to understand how to select the best model to suit your needs. As you advance through this deep learning book, you'll study convolutional, recurrent, and recursive neural networks, in addition to covering long short-term memory networks (LSTM). Understanding these networks will help you to implement their models using Keras. In the later chapters, you will be able to develop a trigger word detection application using NLP techniques such as attention model and beam search.

By the end of this book, you will not only have sound knowledge of natural language processing but also be able to select the best text pre-processing and neural network models to solve a number of NLP issues.

What you will learn
  • Understand various pre-processing techniques for deep learning problems
  • Build a vector representation of text using word2vec and GloVe
  • Create a named entity recognizer and parts-of-speech tagger with Apache OpenNLP
  • Build a machine translation model in Keras
  • Develop a text generation application using LSTM
  • Build a trigger word detection application using an attention model
Who this book is for

If you're an aspiring data scientist looking for an introduction to deep learning in the NLP domain, this is just the book for you. Strong working knowledge of Python, linear algebra, and machine learning is a must.

Table of Contents
  1. Introduction to Natural Language Processing
  2. Application of Natural Language Processing
  3. Introduction to Neural Networks
  4. Foundations of Convolutional Neural Network
  5. Recurrent Neural Networks
  6. Gated Recurrent Units
  7. Long Short-Term Memory (LSTM)
  8. State-of-the-Art Natural Language Processing
  9. A Practical NLP Project Workflow in an Organization
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