Download Elements of artificial neural networks eBook in PDF

(By Chilukuri K. Mohan, Kishan Mehrotra, Sanjay Ranka)



Click here to Download and Read Elements of artificial neural networks eBook

Format: PDF

Language: English

Publisher: The MIT Press



"This most readable book gives a clear, up-to-date and concise introduction to artificial neural networks. It covers all the major network models and provides insightful information on their applications. I thoroughly recommend it to senior undergraduates, first-year graduate students and practising engineers seeking an accessible lead-in to this fast expanding field." -- Duc Truong Pham, Professor and Director of the Intelligent Systems Laboratory, School of Engineering, University of Wales Cardiff, United Kingdom Elements of Artificial Neural Networks provides a clearly organized general introduction, focusing on a broad range of algorithms, for students and others who want to use neural networks rather than simply study them. The authors, who have been developing and team teaching the material in a one-semester course over the past six years, describe most of the basic neural network models (with several detailed solved examples) and discuss the rationale and advantages of the models, as well as their limitations. The approach is practical and open-minded and requires very little mathematical or technical background. Written from a computer science and statistics point of view, the text stresses links to contiguous fields and can easily serve as a first course for students in economics and management. The opening chapter sets the stage, presenting the basic concepts in a clear and objective way and tackling important -- yet rarely addressed -- questions related to the use of neural networks in practical situations. Subsequent chapters on supervised learning (single layer and multilayer networks), unsupervised learning, and associative models are structured around classes of problems to which networks can be applied. Applications are discussed along with the algorithms. A separate chapter takes up optimization methods. The most frequently used algorithms, such as backpropagation, are introduced early on, right after perceptrons, so that these can form the basis for initiating course projects. Algorithms published as late as 1995 are also included. All of the algorithms are presented using block-structured pseudo-code, and exercises are provided throughout.



More ebooks:
Combinatorics for Computer Science ebook
Encyclopedia of Chemical Physics and Physical Chemistry ebook
Evolutionary computation: advanced algorithms and operators ebook
Fundamentals of Neural Networks ebook

Download Elements of artificial neural networks ebook
Download Elements of artificial neural networks ebook pdf
Download Elements of artificial neural networks ebook djvu
Download Elements of artificial neural networks ebook chm
Elements of artificial neural networks ebook
Elements of artificial neural networks ebook pdf
Elements of artificial neural networks ebook djvu
Elements of artificial neural networks ebook chm
Chilukuri K. Mohan, Kishan Mehrotra, Sanjay Ranka ebooks