Natural Language Processing with Python.pdf

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Hands-On Natural Language
Processing with Python
A practical guide to applying deep learning architectures to
your NLP applications
Rajesh Arumugam
Rajalingappaa Shanmugamani
BIRMINGHAM - MUMBAI
Hands-On Natural Language Processing
with Python
Copyright
©
2018 Packt Publishing
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First published: July 2018
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Published by Packt Publishing Ltd.
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ISBN 978-1-78913-949-5
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Foreword
Intelligent digital assistants in form of voice transcription, machine translation,
conversational agents, and sentiment analysis are ubiquitously applied across various
domains to facilitate human-computer interactions. Chatbot's are becoming an integrated
part of any website; while virtual assistants are gaining more popularity in homes and
office spaces. Consequently, given numerous existing resources that contain these topics
under the notion of
natural language processing
(NLP), a contribution that covers a
comprehensive guide to the fundamentals and the state-of-the-art, and on top of that
includes practical examples with most popular frameworks and tool kits, is a rare find.
When I was first asked to write the foreword for this book, I was very delighted to convey
the level of passion that drove the authors to write it, and yet was uncertain on how to best
present an excellent source of up-to-date knowledge and a practical handbook of
machine
learning
(ML) for NLP that truly stands out from the crowd.
The leading authors' reputation in ML needs no further explanation. With both equipped
with academic education in world-class universities and many years of leadership in ML
development, Rajesh and Rajalingappa's qualification to lead the authorship of this book is
confirmed. I have come to know them not only as knowledgeable individuals but also as
passionate educators who convey the most sophisticated concepts in the simplest words.
Raja's passion to help start-ups get off the ground and to offer his expertise to young
companies with an open heart is admirable. I'm sure that, even as readers of this book, you
can approach him for questions and be sure of a convincing response.
The book itself is very well organized and written to serve the purpose. From concrete
examples to explain the fundamentals to code snippets for guiding readers with different
levels of deep learning backgrounds, the chapters are structured to retain full attention of
the reader all throughout. You will discover an exciting combination of the most popular
techniques and state-of-the-art approaches to text processing and classification.
By reading this book, you can expect to learn how to perform common NLP tasks, such as
preprocessing and exploratory analysis of text using the Python's Natural Language
Toolkit. You will understand deep neural networks, Google's TensorFlow framework, and
the building blocks of
recurrent neural networks
(RNNs), including Long Short-Term
Memory. And you will grasp the notion of word embeddings to allow for semantics in
context.

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