Neural Networks and Learning Machines 3rd Edition by Simon Haykin (PDF)

Description

For graduate-level neural network courses offered in the departments of Computer Engineering, Electrical Engineering, and Computer Science. 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. Matlab codes used for the computer experiments in the text are available for download at: http://www.pearsonhighered.com/haykin/ 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.

User’s Reviews

Editorial Reviews: From the Back Cover Neural Networks and Learning MachinesThird EditionSimon HaykinMcMaster University, Canada 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. Just as importantly, the book is written in a readable style that is Simon Haykin’s hallmark.

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐I have bought this book 3 or 4 years ago, but wasn’t able to appreciate the content simply because I wasn’t ready for that level. It’s not a hands-on ML manual, it doesn’t have “let’s do dropout because it proved to be useful and looks like it works like brain neurons” either. It is very dry but very comprehensive mathematical introduction into ML, although it’s a bit old (for example it suggest to use hyperbolic tangent, but nowadays it’s well-known that ReLU less prone to saturation). If you need a book with answers on “WHY” it’s a right book, if you need a book with answers to “HOW” you better find something else.

⭐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.

⭐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.

⭐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!

⭐It’s a great book. Except is assumes good knowledge in computer ( matlab or R ) other than that the book is more the excellent

⭐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.

⭐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.

⭐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.

⭐A good textbook for understand more about learning machine by Neural Networks. The Haykin text is a ‘must have’ for everyone studying this kind of theoretical approach.

⭐A great reference/text for machine learning with a deep mathematical treatment of the subject. It also presents many computational examples. Highly recommended for graduate students with good mathematical background.

⭐Good coverage of contents

⭐The book has been shipped in less time than expected; I’ve bought it on sunday and been delivered on friday. The cover is slightly worn due to the travel, but considering the price and the shipping times I really don’t care at all. Fully satisfied 🙂 5 stars

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