AI for Data Science Artificial Intelligence Frameworks and Functionality for Deep Learning, Optimization, and Beyond First Edition

Master the approaches and principles of Artificial Intelligence (AI)
algorithms, and apply them to Data Science projects with Python and Julia code.

Aspiring and practicing Data Science and AI professionals, along with Python and
Julia programmers, will practice numerous AI algorithms and develop a more
holistic understanding of the field of AI, and will learn when to use each
framework to tackle projects in our increasingly complex world.

The first two chapters introduce the field, with Chapter 1 surveying Deep
Learning models and Chapter 2 providing an overview of algorithms beyond Deep
Learning, including Optimization, Fuzzy Logic, and Artificial Creativity.

The next chapters focus on AI frameworks; they contain data and Python and Julia
code in a provided Docker, so you can practice. Chapter 3 covers Apache’s MXNet,
Chapter 4 covers TensorFlow, and Chapter 5 investigates Keras. After covering
these Deep Learning frameworks, we explore a series of optimization frameworks,
with Chapter 6 covering Particle Swarm Optimization (PSO), Chapter 7 on Genetic
Algorithms (GAs), and Chapter 8 discussing Simulated Annealing (SA).

Chapter 9 begins our exploration of advanced AI methods, by covering
Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
Chapter 10 discusses optimization ensembles and how they can add value to the
Data Science pipeline.

Chapter 11 contains several alternative AI frameworks including Extreme Learning
Machines (ELMs), Capsule Networks (CapsNets), and Fuzzy Inference Systems (FIS).

Chapter 12 covers other considerations complementary to the AI topics covered,
including Big Data concepts, Data Science specialization areas, and useful data
resources to experiment on.

A comprehensive glossary is included, as well as a series of appendices covering
Transfer Learning, Reinforcement Learning, Autoencoder Systems, and Generative
Adversarial Networks. There is also an appendix on the business aspects of AI in
data science projects, and an appendix on how to use the Docker image to access
the book’s data and code.

The field of AI is vast, and can be overwhelming for the newcomer to approach.
This book will arm you with a solid understanding of the field, plus inspire you
to explore further.