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Neural Networks and Learning Machines (3rd Edition), by Simon O. Haykin
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For graduate-level neural network courses offered in the departments of Computer Engineering, Electrical Engineering, and Computer Science.
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Neural Networks and Learning Machines, Third Edition is renowned for its thoroughness and readability. This well-organized and completely up-to-date text remains the most comprehensive treatment of neural networks from an engineering perspective. This is ideal for professional engineers and research scientists.
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Matlab codes used for the computer experiments in the text are available for download at: http://www.pearsonhighered.com/haykin/
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Refocused, revised and renamed to reflect the duality of neural networks and learning machines, this edition recognizes that the subject matter is richer when these topics are studied together. Ideas drawn from neural networks and machine learning are hybridized to perform improved learning tasks beyond the capability of either independently.
- Sales Rank: #339641 in Books
- Brand: Haykin, Simon
- Published on: 2008-11-28
- Original language: English
- Number of items: 1
- Dimensions: 9.20" h x 1.50" w x 7.10" l, 3.17 pounds
- Binding: Hardcover
- 936 pages
From the Back Cover
Neural Networks and Learning Machines
Third Edition
Simon Haykin
McMaster University, Canada
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This third edition of a classic book presents a comprehensive treatment of neural networks and learning machines. These two pillars that are closely related. The book has been revised extensively to provide an up-to-date treatment of a subject that is continually growing in importance. Distinctive features of the book include:
• On-line learning algorithms rooted in stochastic gradient descent; small-scale and large-scale learning problems.
• Kernel methods, including support vector machines, and the representer theorem.
• Information-theoretic learning models, including copulas, independent components analysis (ICA), coherent ICA, and information bottleneck.
• Stochastic dynamic programming, including approximate and neurodynamic procedures.
• Sequential state-estimation algorithms, including Kalman and particle filters.
• Recurrent neural networks trained using sequential-state estimation algorithms.
• Insightful computer-oriented experiments.
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Just as importantly, the book is written in a readable style that is Simon Haykin’s hallmark.
Most helpful customer reviews
0 of 0 people found the following review helpful.
Absolutely terrible textbook
By John
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.
0 of 0 people found the following review helpful.
Great textbook for graduate students
By Andre Lima
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.
33 of 35 people found the following review helpful.
Kindle Edition Riddled With Typos
By W. Birkett
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!
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