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Download Neural Networks and Learning Machines (3rd Edition) fb2

by Simon S Haykin
Download Neural Networks and Learning Machines (3rd Edition) fb2
  • Author:
    Simon S Haykin
  • ISBN:
    8120340000
  • ISBN13:
    978-8131763773
  • Genre:
  • Publisher:
    PHIL; Third Edition edition (2010)
  • Pages:
    936 pages
  • Language:
  • FB2 format
    1616 kb
  • ePUB format
    1221 kb
  • DJVU format
    1965 kb
  • Rating:
    4.7
  • Votes:
    869
  • Formats:
    mobi lit doc azw


Neural Networks and Learning Machines.

Neural Networks and Learning Machines. McMaster University, Canada. These two pillars that are closely related.

Neural networks and learning machines, Simon Haykin . 3rd ed. p. cm. Rev. ed of: Neural networks.

Paperback: 944 pages.

Artificial Neural Networks and Machine Learning – ICANN 2016: 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part I. 585 Pages·2016·42.

Learn Keras for Deep Neural Networks: A Fast-Track Approach to Modern Deep Learning with Python. Artificial Neural Networks and Machine Learning – ICANN 2016: 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part I. 53 MB·1,277 Downloads·New! Conference on Artificial Neural Networks, ICANN 2016, held in Barcelona, Spain, in September 2016

Ideas drawn from neural networks and machine learning are hybridized to perform improved learning tasks .

Ideas drawn from neural networks and machine learning are hybridized to perform improved learning tasks beyond the capability of either independently.

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I. Haykin, Simon Neural networks. The book is accompanied by a Manual that includes the solutions to all the endof-chapter problems as well as computer experiments.

I. Last, but by no means least, every effort has been expended to make the book error free and, most importantly, readable. Simon Haykin Ancaster,Ontario.

Haykin, Simon Neural networks.

Hamilton, Ontario, Canada. We are a sharing community. So please help us by uploading 1 new document or like us to download

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McMaster University, Canada.

ISBN 13: 9780131471399. The book has been revised extensively to provide an up-to-date treatment of a subject that is continually growing in importance.

The third edition of this classic book presents a comprehensive treatment of neural networks and learning machines. The book has been revised extensively to provide an up-to-date treatment of the subject.

Gamba
Great textbook for graduate students. Broad overview and introduction to many relevant machine learning approaches in use or study today. Deep enough to enable the student to follow what prominent researchers have been publishing about machine learning in journals like Nature or Science in the last 5 years.
one life
Haykin is a well known author in this subspecialty and the content of the book seems fine, but it hasn't been updated much from the 2nd edition despite progress in the field. I did fine in a course using the book entirely off a 2nd edition copy and kept this 3e text mostly closed for resale value. The only things I had to note were In chapter 2 it has some updates largely regarding the prevailing Bayesian views and in chapter 8 the problems expanded (old 8.06 is 8.10). As far as presentation, things were done well if sometimes dry (the latter likely because this isn't my area). The required mathematics is presented without tedious re-explanation, but if you aren't prepared for it you might not understand much.
Acrobat
This is an excellent book. However, the Kindle edition is full of errors. There are hundreds of missing spaces and extra hyphens. That is annoying. The serious problem (that led me to write this review) is that math symbols used in the text are frequently wrong and/or indecipherable. This is a math book. These errors are just unacceptable. I'm sure the author would be horrified to see what Amazon has done to his masterpiece!
Jwalextell
It's a great book. Except is assumes good knowledge in computer ( matlab or R ) other than that the book is more the excellent
Madi
This book reads like an angry TA's notebook with no regard for the reader understanding the contents (full of "…it is obvious that…" and "…it follows that…"). I found myself wishing I could raise my hand to ask to explain the examples! In many situations the author brings in seemingly random theorems, attempts to tie them together, and then goes on to use the result without any rigorous proof of the underlying math or the theorems true applicability. It almost feels like the author is begging the question. I understand that, given the breadth of the literature covered, only so much can be said for each topic but the praise this book receives is quite undeserved.

Haykin uses the XOR problem as an example ad nauseam. I realize it's a canonical ANN classification problem; however in later chapters, it's annoying and frustrating when an ANN gets explained in such simple terms but a more complicated classification problem gets a paragraph or two and then a bunch of pictures with no explanation of how to derive the ANN to solve the problem. Detail a "real" problem, or just drop the examples because I can make graphs myself!

Couple this book with a poor lecturer and a student is likely to never pursue ANNs again. The only value I have found in this book is the extensive bibliography. I recommend finding a website that has grouped all Biblio references per chapter and then start hunting down the source articles. You will have to do this anyway to keep up with the author.
Ance
This book is absolutely terrible. It is being used as a graduate level text in neural networks and it is perhaps one of the most abysmally written textbooks ever. The chapter problems are completely out of scope given the material presented and even the solutions manual is poorly written, incomplete, and presented with little or no explanation at all. Many of the solutions do not even answer the questions that they are associated with. The text relies very heavily on esoteric mathematics that are not even remotely explained nor are references even provided to provide foundation. Very few examples are provided throughout the text and the material is nearly impossible to follow.
Conjuril
In general I find the reviews on Amazon.com very useful. Nevertheless, so far this book is collecting a relatively high number of the funniest comments, together with ratings that currently hide a lot of its real value. This third edition has much in common with the classic and more fairly rated "S. Haykin, Neural Networks: A Comprehensive Foundation (2nd Edition)", in particular for its highly technical/mathematical approach. Refer to that book and to its pretty exhaustive and often well written reviews.
Excellent book ... excellent service .. This is a fantastic introduction to neural network ...