Getting started with Deep Learning for Natural Language Processing: Learn how to build NLP applications with Deep Learning (English Edition)

783 Pages·2021·9.245 MB·

by  Sunil Patel

Getting Started with
Deep Learning for
Natural Language
Processing
Learn How to Build NLP
Applications with Deep Learning
Sunil Patel
www.bpbonline.com
FIRST EDITION 2021
Copyright © BPB Publications, India
ISBN: 978-93-89898-11-8
All Rights Reserved. No part of this publication may be
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Dedicated to
My family
About the Author
Sunil Patel has completed his Master’s in Information Technology
from the Indian Institute of Information Technology-Allahabad, with
a thesis focused on investigating 3D protein-protein interactions
with deep learning. Sunil has worked with TCS Innovation Labs,
Excelra, and Innoplexus before joining Nvidia. The main areas of
research were using Deep Learning, Natural language processing
in Banking, and healthcare domain. Sunil started experimenting
with deep learning by implanting the basic layer used in pipelines
and then developing complex pipelines for a real-life problem.
Additionally, Sunil has participated in CASP-2014 in collaboration
with SCFBIO-IIT Delhi to efficiently predict possible Protein
multimer formation and its impact on diseases using Deep
Learning. Currently, Sunil works as Data Scientist – III with Nvidia.
In Nvidia, Sunil has expanded his area of interest to computer
vision and simulated environments, and he extensively works in
the banking, defense, and healthcare verticals areas. Sunil is
currently focused on using GPUs for high-fidelity physics
simulation. He has 3 pending US patents and 4 publications in
the Deep Learning domain. To know more about his current
research topic and interests, you can check out his LinkedIn
profile:
About the Reviewer
Anurag Punia has 6 years of experience in data science and
machine learning, with a special interest in topic modeling,
information retrieval, and named entity recognition under the
subfield of natural language processing. He has worked and
delivered several data science projects across industry verticals, like
insurance, asset management, marketing, tourism, and real estate.
Currently, he is part of the center of excellence of a leading
logistics company in Dubai, UAE. Anurag has a research-focused
BS-MS dual degree from IISER Bhopal with a major in physics.
He can be reached at [email protected] or
https://www.linkedin.com/in/anurag-punia-data-scientist/
Acknowledgements
First and foremost, I would like to thank God for giving me the
courage to write this book. I would like to thank everyone at BPB
Publications for helping me polish it and finally converting my
writing to paperback.
I would also like to thank my parents, wife, and brother for their
endless support and for helping me in numerous ways.
Lastly, I would like to thank my critics. Without their criticism, I
would never be able to write this book.
Sunil Patel
Preface
“The world’s most valuable resource is no longer oil but its data”.
Nowadays, titans and the most valued firm in the world like
Amazon, Google, Apple, and Microsoft have similar concerns as
were raised for oil a century ago. Data is changing the way we
live, and the amount of data generated in the past few years is
more than that generated since human beings have existed. The
amount of data is expected to grow exponentially with the boom
in connected devices, personal assistants, blockchain, and mobile
devices.
The condition for the storage of data is getting favorable, as
storage devices are getting cheaper 3X every 3 years. Hardware
giants like Nvidia already claimed to have broken Moore’s law,
which also indicates the exponential growth in processing power.
Today’s world is highly favorable to the data-centric economy. And
that’s exactly why data is the next oil.
Unstructured and structured data is increasing at a similar rate.
The former comes from a majority of sources, and algorithms are
constantly being discovered to store and assimilate such data.
Unstructured data can be anything, for example, scientific
literature, randomly clicked selfies, chat messages, sensor data
from self-driving vehicles, and voice/video over the Internet. It is
rich in information, but processing such data and training a
machine using such data is challenging. However, advancement
has been made in gaining better understanding of unstructured
data and using such a pre-trained network for supervised learning