Название: Artificial Intelligence Engines: A Tutorial Introduction to the Mathematics of Deep Learning
Автор: James V. Stone
Издательство: Sebtel Press
Год: 2020
Страниц: 216
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB

The brain has always had a fundamental advantage over conventional computers: it can learn. However, a new generation of Artificial Intelligence (AI) algorithms, in the form of deep neural networks, is rapidly eliminating that advantage. Deep neural networks (DNN) 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.

Unlike most books on deep learning, this is not a 'user manual' for any particular software package. Such books often place high demands on the novice, who has to learn the conceptual infrastructure of neural network algorithms, whilst simultaneously learning the minutiae of how to operate an unfamiliar software package. Instead, this book concentrates on key concepts and algorithms.

Having said that, readers familiar with programming can benefit from running working examples of neural networks, which are available at the website associated with this book. Simple examples were written by the author, and these are intended to be easy to understand rather than efficient to run. More complex examples have been borrowed (with permission) from around the internet, but mainly from the PyTorch repository. A list of the online code examples that accompany this book is given opposite. The Python and MatLab code examples below can be obtained from the online GitHub repository. The computer code has been collated from various sources. It is intended to provide small scale transparent examples, rather than an exercise in how to program artificial neural networks. The examples below are written in Python.

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 (e.g. perceptrons), and modern deep neural networks (e.g. generative adversarial networks). 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), and a list of further readings, this is an ideal introduction to the algorithmic engines of modern Artificial Intelligence.

Who Should Read This Book?
The material in this book should be accessible to anyone with an understanding of basic calculus. The tutorial style adopted ensures that any reader prepared to put in the effort will be amply rewarded with a solid grasp of the fundamentals of deep learning networks.

“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, Cambridge University, UK.