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This book offers a comprehensive introduction to the central ideas that underpin
deep learning. It is intended both for newcomers to machine learning and for
those already experienced in the field. Covering key concepts relating to
contemporary architectures and techniques, this essential book equips readers
with a robust foundation for potential future specialization. The field of deep
learning is undergoing rapid evolution, and therefore this book focusses on
ideas that are likely to endure the test of time.

The book is organized into numerous bite-sized chapters, each exploring a
distinct topic, and the narrative follows a linear progression, with each
chapter building upon content from its predecessors. This structure is
well-suited to teaching a two-semester undergraduate or postgraduate machine
learning course, while remaining equally relevant to those engaged in active
research or in self-study.

A full understanding of machine learning requires some mathematical background
and so the book includes a self-contained introduction to probability theory.
However, the focus of the book is on conveying a clear understanding of ideas,
with emphasis on the real-world practical value of techniques rather than on
abstract theory. Complex concepts are therefore presented from multiple
complementary perspectives including textual descriptions, diagrams,
mathematical formulae, and pseudo-code.

Chris Bishop is a Technical Fellow at Microsoft and is the Director of Microsoft
Research AI4Science.

He is a Fellow of Darwin College Cambridge, a Fellow of the Royal Academy of
Engineering, and a Fellow of the Royal Society.

Hugh Bishop is an Applied Scientist at Wayve, a deep learning autonomous driving
company in London where he designs and trains deep neural networks.

He completed his MPhil in Machine Learning and Machine Intelligence at Cambridge
University.


HOW TO ACCESS THE BOOK:

 * Hardback copies can be purchased from online stores such as Amazon or
   directly from Springer.
 * A PDF-based eBook is available for purchase from Springer.
 * A Kindle eBook version is available from Amazon.
 * A free-to-use online version is available below:

Chris Bishop wrote a terrific textbook on neural networks in 1995 and has a deep
knowledge of the field and its core ideas. His many years of experience in
explaining neural networks have made him extremely skilful at presenting
complicated ideas in the simplest possible way and it is a delight to see these
skills applied to the revolutionary new developments in the field.

- Geoffrey Hinton

With the recent explosion of deep learning and Al as a research topic, and the
quickly growing importance of Al applications, a modern textbook on the topic
was badly needed. The "New Bishop" masterfully fills the gap, covering
algorithms for supervised and unsupervised learning, along with modern deep
learning architecture families, as well as how to apply all of this to various
application areas.

- Yann LeCun

This excellent and very educational book will bring the reader up to date with
the main concepts and advances in deep learning with a solid anchoring in
probability. These concepts are powering current industrial Al systems and are
likely to form the basis of further advances towards artificial general
intelligence.

- Yoshua Bengio


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