[DOWNLOAD] Artificial Intelligence Engines A Tutorial Introduction to the Mathematics of Deep Learning in format E-PUB

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[DOWNLOAD] Artificial Intelligence Engines: A Tutorial Introduction to the

Mathematics of Deep Learning in format E-PUB


[DOWNLOAD] Artificial Intelligence Engines: A Tutorial Introduction to the Mathematics of Deep

Learning in format E-PUB

[DOWNLOAD]

Artificial Intelligence

Engines: A Tutorial

Introduction to the

Mathematics of Deep

Learning in format E-

PUB

Description

'authoritative, funny, and concise'Steven Strogatz, Professor of Applied Mathematics, Cornell

University. 'Artificial Intelligence Engines will introduce you to the rapidly growing field of deep

learning networks: how to build them, how to use them; and how to think about them. James Stone

will guide you from the basics to the outer reaches of a technology that is changing the

world.'Professor Terrence Sejnowski, Director of the Computational Neurobiology Laboratory, Salk

Institute, USA. Author of The Deep Learning Revolution, MIT Press, 2018.'This book manages the

impossible: it is a fun read, intuitive and engaging, lighthearted and delightful, and cuts right

through the hype and turgid terminology. Unlike many texts, this is not a shallow cookbook for

some particular deep learning program-du-jure. Instead, it crisply and painlessly imparts the

principles, intuitions and background needed to understand existing machine-learning systems,

learn new tools, and invent novel architectures, with ease.'Professor Barak Pearlmutter, Brain and

Computation Laboratory, National University of Ireland Maynooth, Ireland.'This text provides an

engaging introduction to the mathematics underlying neural networks. It is meant to be read from

start to finish, as it carefully builds up, chapter by chapter, the essentials of neural network theory.

After first describing classic linear networks and nonlinear multilayer perceptrons, Stone gradually

introduces a comprehensive range of cutting edge technologies in use today. Written in an

accessible and insightful manner, this book is a pleasure to read, and I will certainly be

recommending it to my students.'Dr Stephen Eglen, Department of Applied Mathematics and

Theoretical Physics (DAMTP), Cambridge Computational Biology Institute (CCBI), Cambridge

University, UK. Read more James V Stone has worked in Geoffrey Hinton's laboratory at the

University of Toronto, and in Terry Sejnowski's laboratory the Salk Institute, San Diego. He is an

Honorary Associate Professor in Vision and Computational Neuroscience at the University of

Sheffield, England. Read more

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