Deep learning is one of today's hottest fields. This approach to
machine learning is achieving breakthrough results in some of
today's highest profile applications, in organizations ranging from
Google to Tesla, Facebook to Apple. Thousands of technical
professionals and students want to start leveraging its power, but
previous books on deep learning have often been non-intuitive,
inaccessible, and dry. In Deep Learning Illustrated, three
world-class instructors and practitioners present a uniquely
visual, intuitive, and accessible high-level introduction to the
techniques and applications of deep learning. Packed with vibrant,
full-color illustrations, it abstracts away much of the complexity
of building deep learning models, making the field more fun to
learn, and accessible to a far wider audience. Part I's high-level
overview explains what Deep Learning is, why it has become so
ubiquitous, and how it relates to concepts and terminology such as
Artificial Intelligence, Machine Learning, Artificial Neural
Networks, and Reinforcement Learning. These opening chapters are
replete with vivid illustrations, easy-to-grasp analogies, and
character-focused narratives. Building on this foundation, the
authors then offer a practical reference and tutorial for applying
a wide spectrum of proven deep learning techniques. Essential
theory is covered with as little mathematics as possible, and
illuminated with hands-on Python code. Theory is supported with
practical "run-throughs" available in accompanying Jupyter
notebooks, delivering a pragmatic understanding of all major deep
learning approaches and their applications: machine vision, natural
language processing, image generation, and videogaming. To help
readers accomplish more in less time, the authors feature several
of today's most widely-used and innovative deep learning libraries,
including TensorFlow and its high-level API, Keras; PyTorch, and
the recently-released high-level Coach, a TensorFlow API that
abstracts away the complexity typically associated with building
Deep Reinforcement Learning algorithms.
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