PACK | Deep Learning For Natural Language Processing (2019 EN)

Author: Karthiek Reddy Bokka, Shubhangi Hora, Tanuj Jain, Monicah Wambugu
Full Title: Deep Learning For Natural Language Processing: Solve your natural language processing problems with smart deep neural networks
Publisher: ‎ Packt Publishing; 1st edition (June 11, 2019)
Year: 2019
ISBN-13: 9781838550295 (978-1-83855-029-5)
ISBN-10: 1838550291
Pages: 372
Language: English
Genre: Educational: Artificial intelligence
File type: PDF (True, but nonnative Cover), Code Files
Quality: 9/10
Price: 18.99 €


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

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 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 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 preprocessing and neural network models to solve a number of NLP issues.


Learn:
✓ Understand various preprocessing techniques for solving 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

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

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.

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