An Introduction to Neural Networks
| Author | : | |
| Rating | : | 4.29 (585 Votes) |
| Asin | : | 0262510812 |
| Format Type | : | paperback |
| Number of Pages | : | 672 Pages |
| Publish Date | : | 2018-01-02 |
| Language | : | English |
DESCRIPTION:
Not Practical Steven A. Fletcher There's nothing really wrong with this book - it's just not useful for someone wanting to actually program a neural network system.I read all sorts of stuff about the nervous systems in horseshoe crabs, but I don't find myself able to do anything with neural networks. Therefore, I'm scouring the Internet to find some source code examples or a tutorial of some kind.If you want to know miscellaneous information about neural networks, go ahead and buy the book. But if you actually want to construct neural networks, buy something el. An Informative Introduction As the book states, this is an INTRODUCTION, it is not a reference or practical guide to construction. It is rather informative, specifically in the biological sense, and the author does a good job introducing necessary information before using it, such as a review/introduction to vector and matrix mathematics; however, some external reading my be necessary to understand if you do not already understand some of these basics.Note: I have only read the first 1/3 of this book so far as my first book on Neural Networks.In my opinion. Amazing Neural Net Introduction! This is one of the best books I have ever read. It introduces neural networks, with a strong emphasis on biological plausibility. For example, the book compares the visual systems of simple animals with neural network feature extraction. Anderson moves effectively among evolutionary biology, cognitive science, artificial intelligence, and behavioral psychology. His insights are important, clear, and often funny as well. The book gently introduces source code for implementing the various neural networks that he describes.
. About the Author James A. Anderson is Professor in the Department of Cognitive and Linguistic Sciences at Brown University
Anderson is Professor in the Department of Cognitive and Linguistic Sciences at Brown University. James A.
Both cognitive science and neuroscience give insights into how this can be done effectively: cognitive science suggests what to compute and neuroscience suggests how to compute it.. They are introduced to the author's brain-state-in-a-box (BSB) model and are provided with some of the neurobiological background necessary for a firm grasp of the general subject.The field now known as neural networks has split in recent years into two major groups, mirrored in the texts that are currently available: the engineers who are primarily interested in practical applications of the new adaptive, parallel computing technology, and the cognitive scientists and neuroscientists who are interested in scientific applications. It is the only current text to approach networks from a broad neuroscience and cognitive science perspective, with an emphasis on the biology and psychology behind the assumptions of the models, as well as on what the models might be used for. Neuroscience, he points out, provides a rich and valuable source of ideas about data representation and setting up the data representation is the major part of neural network programming. Based on notes that have been class-tested for more than a decade, it is aimed at cognitive science and neuroscience students who need to understand brain function in t