From the Preface:
This book aims to bring newcomers to natural language processing (NLP)
and deep learning to a tasting table covering important topics in both
areas. Both of these subject areas are growing exponentially. As it
introduces both deep learning and NLP with an emphasis on
implementation, this book occupies an important middle ground. While
writing the book, we had to make difficult, and sometimes uncomfortable,
choices on what material to leave out. For a beginner reader, we hope
the book will provide a strong foundation in the basics and a glimpse of
what is possible. Machine learning, and deep learning in particular, is
an experiential discipline, as opposed to an intellectual science. The
generous end-to-end code examples in each chapter invite you to partake
in that experience.
A note regarding the style of the book.
We have intentionally avoided mathematics in most places, not because
deep learning math is particularly difficult (it is not), but because it
is a distraction in many situations from the main goal of this book—to
empower the beginner learner.
Likewise, in many cases, both in code and text, we have favored
exposition over succinctness. Advanced readers and experienced
programmers will likely see ways to tighten up the code and so on, but
our choice was to be as explicit as possible so as to reach the broadest
of the audience that we want to reach.
ID: SC - 1409
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