James V. Stone

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About James V. Stone

James V Stone is an Honorary Associate Professor, University of Sheffield, UK.

Sample chapters at jim-stone.staff.shef.ac.uk/books.html

Books:

Linear Regression (WIth Matlab/Python), 2022.

The Fourier Transform, 2021.

The Quantum Menagerie, 2020.

A Brief Guide to Artificial Intelligence, 2020.

Artificial Intelligence Engines, 2019.

Principles of Neural Information Theory, 2018.

Information Theory: A Tutorial Introduction, 2015.

Bayes' Rule With MatLab/Python/R, 2013.

Twitter: @jgvfwstone

Mike X. Cohen

Will Kurt

Aubrey Clayton

Daniel A. Fleisch

Aurélien Géron

Jay Cummings

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Titles By James V. Stone

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Information Theory: A Tutorial Introduction (Tutorial Introductions) Apr 5, 2018

by James V Stone

( 157 )

$9.95

Originally developed by Claude Shannon in the 1940s, information theory laid the foundations for the digital revolution, and is now an essential tool in telecommunications, genetics, linguistics, brain sciences, and deep space communication. In this richly illustrated book, accessible examples are used to introduce information theory in terms of everyday games like ‘20 questions’ before more advanced topics are explored. Online MatLab and Python computer programs provide hands-on experience of information theory in action, and PowerPoint slides give support for teaching. Written in an informal style, with a comprehensive glossary and tutorial appendices, this text is an ideal primer for novices who wish to learn the essential principles and applications of information theory.

Other Formats: Hardcover , Paperback

Bayes' Rule: A Tutorial Introduction to Bayesian Analysis Dec 3, 2021

by James Stone

( 136 )

$9.95

What does a medical test tell us about the chances of having a particular disease? How can we tell if a spoken phrase is, 'four candles' or 'fork handles'? How do we a perceive a three-dimensional world from from the two-dimensional images on our retinas?The short answer is Bayes' rule, which transforms meaningless statistics and raw data into useful information. Discovered by an 18th century mathematician and preacher, Bayes' rule is a cornerstone of modern probability theory. In this richly illustrated book, intuitive visual representations of real-world examples are used to show how Bayes' rule is actually a form of commonsense reasoning. The tutorial style of writing, combined with a comprehensive glossary, makes this an ideal primer for novices who wish to gain an intuitive understanding of Bayesian analysis. As an aid to understanding, online computer code (in MatLab, Python and R) reproduces key numerical results and diagrams.Stone's book is renowned for its visually engaging style of presentation, which stems from teaching Bayes' rule to psychology students for over 10 years as a university lecturer.

Other Formats: Hardcover , Paperback

Bayes' Rule With Python: A Tutorial Introduction to Bayesian Analysis Apr 4, 2018

by James Stone

( 15 )

$9.95

What does a medical test tell us about the chances of having a particular disease?
How can we tell if a spoken phrase is, 'four candles’ or 'fork handles’?
How do we a perceive a three-dimensional world from from the three-dimensional images on our retinas?

The short answer is Bayes’ rule, which transforms meaningless statistics and raw data into useful information. Discovered by an 18th century mathematician and preacher, Bayes' rule is a cornerstone of modern probability theory. In this richly illustrated book, intuitive visual representations of real-world examples are used to show how Bayes' rule is actually a form of commonsense reasoning. The tutorial style of writing, combined with a comprehensive glossary, makes this an ideal primer for novices who wish to gain an intuitive understanding of Bayesian analysis. As an aid to understanding, online computer code (in MatLab, Python and R) reproduces key numerical results and diagrams.

Stone’s book is renowned for its visually engaging style of presentation, which stems from teaching Bayes’ rule to psychology students for over 10 years as a university lecturer.

Other Formats: Paperback

Artificial Intelligence Engines: A Tutorial Introduction to the Mathematics of Deep Learning Mar 29, 2020

by James Stone

( 47 )

$9.95

"authoritative, funny, and concise"Steven Strogatz, Professor of Applied Mathematics, Cornell University.
The brain has always had a fundamental advantage over conventional computers: it can learn. However, a new generation of artificial intelligence algorithms, in the form of deep neural networks, is rapidly eliminating that advantage. Deep neural networks rely on adaptive algorithms to master a wide variety of tasks, including cancer diagnosis, object recognition, speech recognition, robotic control, chess, poker, backgammon and Go, at super-human levels of performance. In this richly illustrated book, key neural network learning algorithms are explained informally first, followed by detailed mathematical analyses. Topics include both historically important neural networks (perceptrons, Hopfield nets, Boltzmann machines and backpropagation networks), and modern deep neural networks (variational autoencoders, convolutional networks, generative adversarial networks, and reinforcement learning using SARSA and Q-learning). Online computer programs, collated from open source repositories, give hands-on experience of neural networks, and PowerPoint slides provide support for teaching. Written in an informal style, with a comprehensive glossary, tutorial appendices (e.g. Bayes' theorem, maximum likelihood estimation), and a list of further readings, this is an ideal introduction to the algorithmic engines of modern artificial intelligence.
Dr James V Stone is an Honorary Reader at the University of Sheffield, England.

Other Formats: Hardcover , Paperback

Bayes' Rule With MatLab: A Tutorial Introduction to Bayesian Analysis Apr 5, 2018

by James V Stone

( 13 )

$9.95

Other Formats: Paperback

Bayes' Rule With R: A Tutorial Introduction to Bayesian Analysis Apr 5, 2018

by James V Stone

( 13 )

$9.95

Other Formats: Paperback

A Brief Guide to Artificial Intelligence (Tutorial Introductions) Mar 29, 2020

by James Stone

( 4 )

$4.95

Artificial intelligence (AI) has now mastered tasks that until recently could be performed only by humans. These tasks include cancer diagnosis, drug design, object recognition, speech recognition, and playing chess, backgammon and Go, which AI systems perform at superhuman levels. This richly illustrated book is a brief but comprehensive overview (without equations) of current AI systems, how they work, their applications, and their limitations. After surveying the impressive capabilities of AI systems in certain domains, the limited ability of AI to perform tasks that humans find trivial is discussed. Finally, the question of whether AI systems can be intelligent is considered, along with the controversial issue of machine consciousness. Written in an informal style, with a comprehensive Glossary and a list of Further Readings, this book is an ideal introduction to the rapidly evolving field of AI.

Other Formats: Paperback

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