Neural Networks: A Comprehensive Foundation

by Simon Haykin

Provides a comprehensive foundation of neural networks, recognizing the multidisciplinary nature of the subject, supported with examples, computer-oriented experiments, end of chapter problems, and a bibliography. Neural networks (Computer science).

Genres: Computer Science, Science, Artificial Intelligence, Nonfiction, Engineering, Textbooks, Technology

842 pages, Hardcover

First published January 1, 1994

About the author

Simon Haykin was a Canadian electrical engineer noted for his pioneering work in Adaptive Signal Processing with emphasis on applications to Radar Engineering and Telecom Technology. He was a Distinguished University Professor at McMaster University in Hamilton, Ontario, Canada.

Reviews

[Ali Frz · Rating 5 out of 5]

امیدوارم پاس بشم:)
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هیکین (حیکین) نویسنده فوق العاده ای است، مخصوصا این کتاب بسیار استادانه نوشته شده

[JcJet · Rating 4 out of 5]

The title of the book is very accurate: A Comprehensive Foundation. The book is big, and it gives a good understanding of how various types of ANN works, for which tasks they're best suited for.
It helped me when I was on the first year of writing my PhD thesis, to get a broader view on this field.
But as the title says, it's a good foundation. For an actual task you need to do further research on recent achievements in the architectures, and their implementation in code.

[Michael Sazonov · Rating 4 out of 5]

Сложная, масштабная, интересная книжка.

[Nandhini · Rating 2 out of 5]

Important book for my studies

[Craig Martin · Rating 4 out of 5]

I bought this book at the turn of the Millennium in Borders, Singapore, for the princely sum (back then) of S$165.99. At the time, it was the second most expensive book I had ever bought (the first was the Memoirs of Sir Stamford Raffles, purchased for A$1,000 from an antiquarian bookshop in Canberra, Australia).

I was about to graduate with distinction from INSEAD and wanted to apply my knowledge of finance to my understanding of neural networks. I had 'discovered' neural networks a decade before, in 1990, when I programmed the University of York's MIPS machine to self-organise digitized pictures of facial expressions. I wrote a Kohonen Map from scratch in C. In 2000, I had forgotten about neural networks, so I picked up Haykin's book as a refresher.

It is a book that has stood the test of time, and I dip into its pages now and then. It gives a detailed mathematical basis for the various types of machine learning. I am amazed to see the relevance of several of the models described in more recent neuroscience areas, even 25 years after the book's publication.

I gave it Four Stars.

[Tolga Durgun · Rating 4 out of 5]

It is important for my course

[Huma]

neural net