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Deep Learning for Natural Language Processing - Solve your natural language processing problems with smart deep neural networks (Paperback)
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Deep Learning for Natural Language Processing - Solve your natural language processing problems with smart deep neural networks (Paperback)
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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
DescriptionApplying 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 forIf 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.
General
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