Neural Networks Books

I took the Microsoft exam 70-216 for network infrastructure today and all I can say is AMAZING! How did the writers know what was on the exam? There is so much obscure stuff on the exam that no other book I read covered the questons on the exam. But this one did. So much of the test was troubleshooting the network, so I guess a TCP/IP troubleshooting book would be the right one. But the similarity of this book to the test is amazing.

This book was good to read too and I am using it at my job and fixing some of the problems we've had with WINS and VPN based on what I learned. Great book and best study guide for the test.

This book is heads and tails above any other TCP/IP book I've read or own. Finally understand how DNS works, the RAS section helped me put together my Win2k VPN. Get this is you wnat to understand some of the weird stuff in Win2k TCP/IP.

We are in the process of moving from NT to Win2k and my boss made me the project manager. I had to get on top of Win2k networking fast. I bought this book on the recommendation of several of my co workers. Glad I got it. The book is informative and detailed in explanations and examples. A must have for the busy guy like me.

OK, I admit it. I learned my TCP/IP for Windows NT exams from reading Exam Cram. Needless to say, I passed the Windows NT TCP/IP test, but couldn't tell a subnet from a supernet. Now I have a job in the industry and I needed to actually learn TCP/IP, especially since we are moving up to Windows 2000 in our shop.

This book is unreal in how good things are explained. Great detail in describing RRAS, WINS, DNS, and the TCP stack. Using the information in the book I am now up to speed on TCP/IP. Enough to pass the 70-216 test! Not bad for a NT MCSE!

For Real, this book helped a lot. I owe the author's a beer on this one.

This book is fresh air to someone like myself who has read at least a dozen Windows 2000 books. I get the impression that a lot of the Windows 2000 books were written by people who write books and don't work with the technology. This book doesn't fall into that class. It was great to read this book, because it renewed my faith that a tech book could be written in a way that doesn't put me to sleep.

They cover Windows 2000 TCP/IP from top to bottom. WINS, DNS, DHCP, RRAS, IIS, routing and network devices. Its all there, and its filled with little known factoids that makes me want to keep reading and have another "aha!" experience.

This book also was the major reason I passed the Microsoft 216 exam so easily. Although I didn't buy it to pass the exam, they seem to cover all the material that the exam covered. A nice bonus. I wish they made the book longer, because I'm sure they could have said a lot more that I would like to read about.

This book isn't for beginners, but neither is Windows 2000. I think once the reader is ready to manage Windows 2000, they'll be ready to get the most out of this exceptional book.

This is a recommended book to read for people who would like to read about statistics and maths. People with few knowledge about these sciences will find it a bit difficult to read.

This is the best book I have found for a general study of the of neural networks. I found this particularly useful when looking at how to write my own NN frameworks. The depth of the mathematics allowed me to easily answer questions like: 'what if I replaced function abc with xyz'. I have found other texts failed to show key mathematical derivations, or to explore the subtleties of what the maths imply.

The book covers a plethora of topics from simple gradient descent through second order techniques and conjugate gradient, through to the use of 'bayesian techniques' (basically confidence intervals on network outputs), monte carlo techniques etc. Similarly error functions, non-linearities (sigmoids, softmax etc.) and data preparation are all treated.

The extensive bibliography also provides excellent references for further study, (a whos who of the field, as well as actual titles). My copy is now dog earred from frequent reading.

The theories of NN and PR are quite difficult to understand. But this book makes them much easier. The author can explain the concepts without using too much formula. If other authors could follow his step then the life is much easier!

Mr Bishop's book is very well written and contains a lot of useful information on neural networks. It is outlined well and progresses in a logical form. If, however, you are looking for a book that gives discussions with concrete examples of neural networks applications or set ups, you will be sorely disappointed. The mathematical treatment is universally generalized with very few specific concrete examples shown. Even the exercises will not serve you well. The term 'graded' is used; however, that simply referes to the description of difficulty. There are no answers to these exercises, so unless you have a teacher or are already firmly familiar with the material, you will not know if you have completed them correctly or not. Even worse, the exercises are in general not written to reinforce concepts in the chapter, but in most cases extend the chapter material into new regions.

In summary, this book should only be purchased by someone already familiar with neural networks and their mathematical basis. Anyone else will be wasting their money.

If you want a very good, intermediate introduction to pattern classification this book must be on your bookshelf. It even does a very nice job explaining the EM algorithm in a few pages! Basic calculus is all you need to understand the book. A must read.

I had the pleasure of not only using this text for my neural network design graduate course, but also taking the course from one of the authors (Professor Howard Demuth).

This book is extremely well written. Being a PhD student in computer engineering, I have read many math books and advanced engineering books. Most of these books are informative, but difficult to read. Much of this is understandable because the topics are complex and explaining them in a very simple manner requires significantly more time. More diagrams, more examples, rewriting paragraphs to improve clarity, etc. This book tackles all of those issues perfectly!

Right now I am reading one of the other "classic" math texts and while I am already familiar with the topic, the reading is extremely difficult. Due to this, I recalled how easy it was to understand the neural network design text and wished my current author wrote more like them.

If you are interested in machine learning, in particular, neural networks, this is a superb book to get you started. Even the most complex mathematical topics in linear algebra and network design are explained so almost anyone can understand. Even if you do not have a strong mathematical background, you'll be able to understand almost all of the math.

Excellent book - (5/5 stars)!

I knew the very poor Matlab Neural Network Toolbox User's Guide by the same authors and I was kind of expecting the same, and boy was I wrong!

This book is simply brilliant, a miracle of pedagogy. It is intended for undergrad classes, but it is so clear that graduate students will benefit enormously from reading it before any other material. Plainly put, this book makes you UNDERSTAND this difficult topic, more than any other book that I know of (Zurada, Smith, Hassoun, Haykin, Duda-Hart, Caudill, etc)

A selection of worked out problems are included at the end of each chapter, a practice that is highly beneficial but alas too rare in books of the kind.

I very much appreciated the very clear exposition of backpropagation, and optimization methods such as Levenberg-Marquardt.

A note to Matlab users: funky demos are available for free and illustrate the main points of the book.

I purchased this book while looking for an appropriate textbook for use in my class on neural networks. This book is excellent for both beginners and experts. It is a rare book in that it demonstrates complex mathematical manipulations and principles (that are difficult to grasp and visualize - and explain) using examples. The review on mathematical principles is very useful. The book makes it easier to teach the subject now. Given the way everything is presented, this book will also help those that want to code their own networks. I recommend this book to everyone.

Very useful book to have if you plan to use NN (specially in matlab environment). The theory is very well covered and explained, along with graphical examples. The book lacks however, more insight into how to implemen NN in real world problems (for that, I would suggest seeing Fundamentals of NN by Laurene V. Fausett).

I have been studying neural network design for almost 30 years now and I have never found a more enchanting text book than this one. From day one I could not put it down. In fact, I bought a copy for each member of my extended family. Beale is brilliant in the way he demostrates the design and capability of neural network systems. No one has ever captured the public's imagination and heart the way he does in this compelling work. He has taken the study of neural networks to the next level. Hence, the world will be a better place. H2BurBabes4Ever.

After owning this book for many years and reaching for it many times, I decided that I at least owed the book a five star review.

Early in my graduate career I began working with neural networks and discovered this book in a electronic bookshelf available at my university. After printing chapter after chapter to read on subway rides home I ended up buying it for convenience. It gave me the background I needed to code up a basic artificial neural network in C++ and to then extend it to fit my needs.

The style of the writing is the perfect balance of enough detail to understand a concept or method without unnecessary wordiness. Each chapter covers an important aspect of neural network development and application - for exmaple, internode weight initilaization techniques - and acts a sort of mini-review of the most popular methods with a clear explanation of the pros and cons of each.

This is an excellent bookshelf addition for anyone who works with neural networks.

Everybody who tries to use NNets for real goes through these steps.
First, there is the Delta rule.
Then, there is overfitting, local minima, generalization problems and frustration.

The complexity of NN is not in it's math; the difficulty is in the construction of a NN. This book is excellent in providing rules-of-thumb for NN construction, while at the same time providing the theoretical backing.

Hey I am not making money reviewing this book, it's just really good.

Many textbooks can help me to understand the different concepts of neural network, but not the practical tips needed to optimize neural network anlysis and implementation.

The topics covered are reminicent to those discussed in part 2 and 3 of the Neural Network FAQ. In chapter 6, the relationships between learning rate, momontum, trainig time and learning modes are presented graphically. With this, it helps me to rule out and avoid learning parameters that are unlikely to improve the NN performance. This is especially important if the dataset is large and the NN program is implemented in Java.

If the aim is to develop a NN solution that will give you the best results, I find both chapter 7 (heuristics for weights initialization) and 16 (heuristics for improving generation) are esential and saves me a lot of time from reading many journals.

In summary, this book has helped me to develop the art of NN optimization. It shows me how to visualize decision surface and the various graphical relationships between learning paramters and various components of NN topology. I think you will find this book very useful after your NN program is up and running and you are looking for ideas and explaination on how to improve the NN performance further.

Some books just have the right feel about them and this is one of them. The author clearly knows neural networks and also knows how to communicate to others in a no-nonsense fashion. With so many books being published, you rarely find a technical book that is as good as this. The emphasis here is on conveying the insights that the experts in this area would know. Importantly, concepts are explained equally in words, graphics and mathematics, maximising the uptake of knowledge from the book. Tufte would be impressed by the quality of the line graphics in this book, and the information that they convey, not to mention the overall presentation. I suspect that this book would make an ideal textbook for a course in neural networks. Overall, I've enjoyed reading it very much.

Book is excellent. Covers the theory very well, such that you can make the computer code yourself. They also provide puedocode. You will be able to learn it better than other books that just give you the code. I find that once you understand the theory, writing the code is easy.

Eases the reader gradually through the foundations of ICA and treats various published methods in a contrasting manner. No other reference is needed while reading the book; he even gives the pronounciation of some of the greek letters in footnotes.

This excellent book introduces the reader in the field of Independent Component Analysis providing the necessary fundamentals to understand and apply the different methods. The book also makes interesting links to other techniques. The author has succeeded at writing a very didactic text, not an easy task given the complexity of the matter, and at transmitting his enthusiasm to the reader.
I've enjoyed this book, which has been not only an introduction to ICA but which has brought me into ICA, stimulating my own experimentation with the technique.

Without this book I would never have understood the basics and finesses of ICA. Even if readers ar highly skilled in math reading this book will set out mile'stones' that will enhance the understanding of the ICA- problem, -tools and -possibilities.

Dr. G. Otte

I can't stress how reader friendly this book is. It is by far the best introduction on component analysis. It is written in such a way that those with a weaker math background can understand it while those with years of experience will not be bored, at certain times it even reads like a story.

It addition to being readable the book contains an impressive amount of content for its size. This content is presented in an organized manner, and in such a way that the user can immediately apply the techniques to their own problems.

If you are interested in independent component analysis or one of its relatives I highly recommend this valuable, reasonably price book.

James Stone's monograph is a refreshing new book amongst the many other `new books' on Independent Component Analysis (ICA). The author brings his teaching experience to present the theory and practice of ICA in a highly accessible form using a duplication of words and straight-forward mathematics.

Particular attention is given in the earlier chapters to the description of the linear signal mixing process giving the Reader a good basis for understanding the fundamental assumptions upon which ICA and its application to Blind Source Separation are based.

The book is aimed at the Reader with a technical but not necessarily formal mathematics background. Illustrative examples and functional algorithms in MatLab are frequent and references are made to the author's available electronic resources. As such it is suitable to both the newcomer to ICA, and to the more expert engineer or scientist.

This Reviewer rates this book very highly.

If you have been putting off getting your MCSE and am anything like I was, "Next month I will start preparing for my MCSE.". However, I had no place to begin.

This is the place to begin. Buy this book, READ this book, and you WILL have a better knowledge of computer and network basics.

If you are currently working in this field, you'll even understand some of the things you've troubleshooted in the past - it all comes clear.

I am a programmer for a small software company. Besides programming, I also do training and I fly places and install client/server database engines (and our app) on various networks; which means that I end up troubleshooting misconfigured networks. We have tech support people who talk to IS people at different companies every day. I have recommended to my boss that he buy a copy of this book for every one of our techs. This book uses clear language and covers all the relevant subjects. It is well-organized and almost free of errors. Kudos to Lisa Donald!

This book is destined to be a classic.

Who's saying this? A guy who's an MCSD and a Sun Certified Java Programmer.

I am not the most educated person in the field of computers or networking, but I am not the stupidest either. I build computers on the side and got interested in getting my MCSE. I got this and 3 other books, this book really starts with the basics. If you are getting your MCSE, you will need more than this, however this will definintly prepare you for the acronyms and vocabulary you will find in more advanced books, so that you will know what they are talking about. It is a priceless reference to me and find myself referring back to it time and time again. I have 2 books of the Sybex MCSE titles and they are both very good, I have not rated the other nor will I mention it now because I have not yet finished it. If you want to get your MCSE I recommend this book. It is fast easy reading and barring what it deals with is the most attention grabbing non-fiction book I have ever read.

This book is extremely simple, but excellent background for those who have little or no knowledge of computer networks and Windows NT. I found it very clear and the descriptions of basic Networking model is much better than any I have found in university course materials. I had never used NT so was pleased to find that this described the basic principes. If you know the basics already, skip it and go straight to the Sybex study guides.

I AM A 56 YEAR OLD TRUCK DRIVER WHO DECIDED TO BECOME A MCSE. WHILE TAKING MY CORE COURSES I FOUND MOST ARE FOR PEOPLE WHO HAVE WORKED IN THE FIELD AND WAS VERY, VERY LOST.. THANKS TO YOUR BOOK I HAVE FILLED IN MUCH OF THE BLANKS IN BACKGROUND EXPERIENCE. THIS BOOK WAS SUCH A FIND FOR ME..I HAVE GIVEN THIS BOOK TO MY 15 YEAR OLD SON TO READ TO PREPARE HIM FOR A CAREER IN NETWORKING.. WOULD RECCOMEND A CD-ROM/SEARCHABLE DATABASE ADDITION WITH A ENCYCLOPEDIA OF NETWORKING TERMS AND CONCEPTS.. WHAT A GREAT BOOK!

Wireless Intelligent Networking is an excellent publication with rare characteristics: easy to read, well organized and extremely efficient in what concerns quantity and quality of information. Technical protocols, market trends, services deployment, integration, among other IN hot issues are thoroughly dissected in WIN. This is definitely an essential tool for anyone in the Telco business, saving time and money in training for beginners and helping experts on a daily basis. A "dot.safe" investment for any company in the industry!

The telecommunications industry needs a lot of standards and
detailed information for true inter-operability. So, no book
is going to be perfect in its coverage of the material - there
is simply too much information.

Having said that, I find this book to be an excellent way to
understand the issues associated with WIN. It provides the basis
for further study and points people in the right direction for
increasing their knowledge.

I use this book as a basic reference and recommend it highly.
You will not go wrong reading this book - whether you are a
wireless telecom professional (which I am) or not.

The book succeeds in presenting an inherently complex and multi-disciplinary subject in a clear manner with sufficient references to allow further study as needed.

The book provides a broad view of wireless networking, including financial, market, and technical views. The technical information is well organized and presented from more than one perspective. Rather than presenting volumes of minute details, architectural principals are introduced and illuminated.

This is one of the outstanding technical books that I have ever read. I would highly recommend it to experienced hands in the fields of wireless or wireline voice networks.

I would also recommend it to beginners with the following cavaet: this book plumbs some fairly deep waters, and does not delve too deeply into the related fields that are the building blocks of Wireless Intellegent Networking. SS7, AIN, PSTN architecture, and mobility management are all presented, but having some previous background (or somebody handy who can fill in details) would be a big help.

Wireless Intelligent Networking is an excellent publication with rare characteristics: easy to read, well organized and extremely efficient in what concerns quantity and quality of information. Technical protocols, market trends, services deployment, integration, among other IN hot issues are thoroughly dissected in WIN. This is definitely an essential tool for anyone in the Telco business, saving time and money in training for beginners and helping experts on a daily basis. A "dot.safe" investment for any company in the industry!

This is a unique book that compares the intelligent networking standards, the ANSI-41 and GSM. It not only provides useful technical information but also covers the issues pertaining to the wireless intelligent market. I would definitely recommend this book to anyone who is interested in WIN and CAMEL.

This book can be viewed as one of the first attempts to use results from psychology, neuroscience, computer science, and philosophy with the intent of gaining an understanding of how the mind/brain works, but all of this is done within the "computational mind" paradigm. The approach taken by the authors is one of the most honest of those in the literature, for throughout the book they are careful to note just how much evidence there is to support their position(s), and to what extent further work is to done. Philosophically speaking, the authors are clearly in the materialist camp, believing that Cartesian dualism does not cohere with current scientific knowledge. But they state that materialism is not an established fact, allowing the possibility, but not the probability, that dualism may in fact be true. They reject early on though any "arguments from ignorance" in their assertion that just because neuroscience does not have an explanation of consciousness, that such an explanation is impossible. The authors call the failure to be able to think of consciousness in terms of neuronal activity "intuition dissonance", and reject completely its efficacy in establishing the truth of the nature of the mind/brain.

The underlying theme in the book is to explain emergent properties as "high-level" effects that are dependent on "lower-level" phenomena, hence rejecting the thesis that they are "nomologically autonomous", i.e. that such a dependence cannot be done and is outside the domain of science. The science in this book recognizes its historical origins, and it is clear that the authors will not accept explanations of the mind/brain that do not involve scientific experimentation and analysis. Much has been done experimentally in neuroscience since this book was published, especially using the techniques of magnetic resonance imaging (MRI). A brief discussion of MRI is given in the Appendix of the book, but no doubt if the book were updated there would be a lengthy overview of it. The current experimental situation in neuroscience has led some to predict a total "reverse engineering" of the brain in the upcoming decades. This prediction is an optimistic one, but no doubt detailed knowledge of the brain will continue to accelerate, this being a sign of what the authors call "a remarkable time in the history of science".

The authors devote an entire chapter to the computational modeling of the brain, mostly of course dealing with the mathematics of neural networks. The approach in this chapter though is still at a level that would allow a general audience to follow it. Readers with a background in physics, especially statistical physics, will appreciate more the discussion on Hopfield networks and Boltzmann machines. Experimental results are inserted as graphs throughout the book, with detailed explanation. As a whole the discussion of the biology of the brain is purely descriptive, and the line drawings could stand some improvement.

The chapter on neuronal plasticity is the most interesting in the book, the authors viewing the brain as an entity that is continuously undergoing modification. Their stated goal in the chapter is to explain how the "local" property of plasticity can result in the "global" property of learning. Clearly intelligence to the authors is an emergent property, i.e. an object or device may be characterized as intelligent without its components being intelligent. Particularly interesting in this chapter was the discussion of the amnesia of a patient who underwent bilateral resection of mesial temporal lobe structures. The time scales of the patient's memory are striking: he remembered things before the surgery but could not remember things that happened a few minutes or hours ago, but could remember things within a minute in his past. The authors also mention the fascinating work of Antonio Damasio and his collaborators, this research being even more important at the present time. The scientific study of consciousness is just beginning and no doubt this study will give many surprises as it develops throughout the twenty-first century.

This extremely interesting book integrates our vast knowledge of neuroscience with computational models of perception, sensori-motor integration, memory etc.
For students of neuroscience, computer science and psychology this book is extremely important, because it gives you the necessary fundamentals of this field(namely computational neuroscience) so you can get to more advanced levels easily.
Understanding the book will need some background in higher mathematics (differential calculus).

There is an argument that this is a book of its time. It is nearly fifteen years since it was put together and a great deal of neural water has flowed under the bridge. The thematic enthusiasm for computationalism that dominates the book has not been convincingly proved in the meantime. If anything, the computational properties of models have been shown to entertain many unpleasant complexity results. Moreover, the localisation of brain functions grounded in naive interpretations of lesion effects has come under greater scrutiny due to detailed MRI results. Given twhat was known at the time, it is unsurprising that the book focuses on the visual system - a focus also found in Christof Koch's recent book. Acknowleding all that and more, it would be hard to find a better condensation of science, computationalism, and philosophical speculation than in this book.

Leaving aside downsides arising from recent discoveries that the authors could not have anticipated, the book can be frustrating to read at times. In particular, there is a tendency to introduce technical concepts and descriptors into accounts without prior definition. For example, very early on in a brief account of monkey vision there is mention of V4, MT, etc. The terms are neither defined nor explained. Strangely, in the introduction to networks, the inner product of two vectors is explained while the outer product is not. Small points but the oversight recurs.

The philosophical content in the book is light, but the assumptions driving the work are among the most contentious. There is no point reaming off a list but the book does not shirk supporing the brain-as-a-computer hypothesis.

All in all a stimulating work, if in need of updating.

This book is an excellent follow-up to "Engine of Reason, Seat of the Soul". It gets into the details of what's known about computational neuroscience and the working of the human brain (and other animals ' brains and nervous systems. Great summaries of specific "tricks" of brain processing, e.g., how vison works, etc.

Netlab implements in Matlab most algorithms presented in Christopher Bishop's excelent book Neural Networks for Pattern Recognition

Nabney's book is an indispensable guide if you want to go into the inner workings of Netlab.

Recommended.

Amazingly compact book and MATLAB toolbox that provides an exceptionally clear introduction to a core collection of pattern recognition tools. This text and the related MATLAB toolbox ostensibly accompany Chris Bishop's text _Neural networks for pattern recognition_ and brilliant as that book is, this is the perfect supplement that does so much more than just implement the algorithms (and not just for MATLAB users but for anyone who appreciates the merits of learning by doing). From the introductory chapters on MATLAB syntax and optimization (clearer and more useful than the _Numerical Recipes_ version) you know you are in for a breathless ride but the examples and demos are perfectly chosen to illustrate the relative merits of the different approaches under consideration.

The chapter titles are

1. Introduction
2. Parameter optimisation algorithms
3. Density modelling and clustering
4. Single layer networks
5. Multi-layer perceptron
6. Radial Basis functions
7. Visualization and latent variable models
8. Sampling
9. Bayesian techniques
10. Gaussian Processes

The MATLAB code is elegant and well-commented and lends itself to endless tweaking and experimentation. I wish I had written this book. Congratulations to the author and hope there is another book on the way.

This is actually a must-have book for those who want to study pattern recognition.

i first bought the book by Bishop (Neural Network for Pattern Recognition) and anyone who have read it can tell u how excellent that book is. This book has a little bit less theory and more on implementation which is perfect for me. This book include all the topics covered in Bishop and then some. How the book is organized, and how concise, easy to understand the material is at the same amazing level as Bishop's. I believe implementing and practicing things u learn is key to understanding them.. if you just look at how things are implemented, things would suddenly become 10 times clearer for you.. often to your own amazement (that you can actually understand all those stuff). this book is extremely useful even if u dont have matlab (just look up the syntax at mathworks web site), cuz matlab code is straightforward to understand. and the material included is very up to date and cutting edge indeed. i highly highly recommend it.

I learned about Neuro Linguistic Programming

and the different strategies or meta-strategies.

It's not for the faint-hearted.

It's very, very logical.

It doesn't do well for all those intuitives out there!

Once you have flexibility, you like Robert Dilts

great distillation of innovative ideas.

I can't wait to train with him in the near future.

For computer people as well who like to get a way from the computer and into something more human.

If you like lockpicking, this book is about how to

distinguish the different locks in people's representational systems and personalities.

I thoroughly enjoyed the section about Modelling

and the Strategies section.

For human resource trainers this is a must read.

You will find the Design section a hoot!

Also for those physicists and mathematicians and doctors who like to map our personality and the genius behind life strategies.

Own this book! for your growing Neuro Linguistic Programming book collection.

I gave one to my mentor professor,Dr. Barclay at Michigan State University!

This is the woulderful book

Ever wonder how people do things with their personal strategies? Robert Dilts crafts a great book about the different strategies and meta-strategies in learning and beyond. The beginning neuro linguistic programmer will learn about the different representational systems: auditory, visual, kinesthetic, gustatory and olfactory. How do people run their motivational or inspirational strategies for example ? One may imagine learning something quite useful for figuring out the lock in someone's personality. Elegant book for trainers who have to convince or train people with new material. Learn about how to discover people's strategies with this book. It's very logical and very scientific. Not a book for intuitive types! This is pure research and useful strategy about strategies in the world of neuro linguistic programming. Ever wonder how successful people become successful people? Here's the toolbox!

This book is by one of the first wave of NLP scholars and innovators. Of them all Dilts and John Ginder were the two real scholars in the group. Dilts in this book shows that not only is he an innovative scholar and therapist but an author who can take material which can be complicated and explain it clearly and in a interesting way.

If you are interested in what NLP is and what it can do, this book will give you many of the basic patterns of practice. While Bandler and many of the other authors are entertaining, this book will give the patterns in clear prose without self promotion. Most of the classic texts and books will give you less. This is not like some of the books which give one or two patterns in the entire book. It rather gives pattern after pattern after technique in clear and understandable prose without water time or space.

If you want to learn it, this is the book. You will find few that give as much information as clearly.