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Preface
Fifth, the advent of new AutoML techniques (Chapter 14, An Introduction to AutoML)
that can enable domain experts who are unfamiliar with ML technologies to use ML
techniques easily and effectively. AutoML made it possible to reduce the burden
of finding the right model for specific application domains, spending time on finetuning
the models, and spending time in identifying – given an application problem
– the right set of features to use as input to ML models.
The above five trends culminated in 2019 when Yoshua Bengio, Geoffrey Hinton,
and Yann LeCun – three of the fathers of Deep Learning – won the Turing Award
"for conceptual and engineering breakthroughs that have made deep neural
networks a critical component of computing." The ACM A.M. Turing Award is an
annual prize given to an individual selected for contributions "of lasting and major
technical importance to the computer field." Quotes taken from the ACM website
(https://awards.acm.org/). Many are considering this award to be the Nobel of
computer science.
Looking back at the previous eight years, it is fascinating and exciting to see the
extent of the contributions that DL has made to science and industry. There is no
reason to believe that the next eight years will see any less contribution; indeed, as
the field of DL continues to advance, we anticipate that we'll see even more exciting
and fascinating contributions provided by DL.
The intent of this book is to cover all the above five trends, and to introduce you to
the magic of deep learning. We will start with simple models and progressively will
introduce increasingly sophisticated models. The approach will always be hands-on,
with an healthy dose of code to work with.
Who this book is for
If you are a data scientist with experience in ML or an AI programmer with some
exposure to neural networks, you will find this book a useful entry point to DL
with TensorFlow 2.0. If you are a software engineer with a growing interest about
the DL tsunami, you will find this book a foundational platform to broaden your
knowledge on the topic. A basic knowledge of Python is required for this book.
What this book covers
The intent of this book is to discuss the TensorFlow 2.0 features and libraries,
to present an overview of Supervised and Unsupervised Machine learning
models, and to provide a comprehensive analysis of Deep Learning and Machine
Learning models. Practical usage examples for Cloud, Mobile, and large production
environments are provided throughout.
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