Neural Networks Books

Emergent research topic of biomimetic electronics is presented in structured and easy to understand manner. Book is highly recommended to any interested person ranged from professional to beginner.

A programmable digital computer, based on Alan Turing's design, ran its first program in 1950 at the National Physical Laboratory, London. Even today, every computer in the world remains computationally equivalent to a Turing Machine. It is little known however, that Turing also investigated neural network architectures as early as 1948, and before the term genetic algorithm was coined, proposed configuring his networks with a "genetical search". In this book Teuscher presents the most extensive exploration of Turing's neural networks available. The book contains over 100 diagrams, detailed examinations of the logical behaviour of Turing's networks, experiments into their emergent properties and extensions of Turing's ideas based on recent findings. An understanding of Turing's networks allows insight into a number of modern research areas such as Kauffman's work on the principles of self-organisation, the boundaries of computability, and even the real neural networks of living things (Turing claimed that his neural networks were probably the simplest possible model of the human cortex). Because the discussion in the book starts with Turing's early networks and progresses through to current research, it can also be read as an accessible overview of the history of the field. In addition, the book makes it clear that there are many interesting research questions still to be answered in this area. As such, this book will be of interest to historians of computer science and modern researchers alike.

This is an absolutely brilliant work focusing on both fundamental aspects of Fuzzy logic systems. Firstly an intuitive and physical understanding essential for diverse applications of fuzzy sets is developed while at the same time mathematically sound treatment of fundamentals is carried out. There is no other book which integrates clarity and the underlying mathematics to such high level. The treatment of type-2 fuzzy sets is unrivalled by all standards. Mendel is perhaps first time ever in a published book, gives a truly geometric and easy to understand description of type-2 fuzzy sets, making lives of PhD students like myself easier. However, book can be read and used for applications by any one with undergraduate degree in science, engineering or even finance etc and requires moderate mathematical maturity. All relevant mathematical aspects of fuzzy logic systems are covered in detailed in the book itself.

The book begins with a comprehensive and deep treatment of type-1 logic system or in other words, ordinary fuzzy set theory. Then type-2 fuzzy is introduced early on with extremely easy to understand format. While theory of ordinary fuzzy logic is developed to the full extend in part 1, part two of the book, focuses on parallel development of type-2 fuzzy sets. There are no other works, except perhaps Mendel's own published papers, which have such easy to understand and easy to internalized treatment of relatively new and difficult area of type-2 fuzzy logic system. Throughout the easy to understand examples with detail applications of type-2 fuzzy logic system to engineering ranging from digital communications to knowledge mining are given.

While mathematical aspects are covered in detail and without sacrificing required rigor, overall style is quite informal and very easy to read. The book successfully advocates the use of type-2 fuzzy logic systems as a generalization of type-1 fuzzy logic system while demonstrating increased applicable power of type-2 fuzzy systems to tackle difficult engineering and scientific problems including but not limited to time varying, systems, systems involving non-stationary noise processes and nonlinear systems.

Relevant examples of above areas are considered in detail along with reasons to use type-2 fuzzy systems as a solution mechanism. Examples of application areas covered include equalizations of nonlinear and time-varying digital communications channels, rule based classifications of video traffic, knowledge mining using IF-THEN questionnaires and so on. This aspect makes this book quite unique and allows easier transition from described theory to the required applications for the reader.

In the final analysis, this is the perhaps the only book available, which while stressing mathematical details relating to type-1 and type-2 fuzzy logic systems, provides physical and intuitive understanding drawn from detailed discussions and heavily supplemented with figures and pictures with remarkable clarity as well as gives easier to follow practical application based examples. All three aspects work together in a synergetic manner to clarify and imprint deep knowledge relating to fuzzy systems in the minds of the readers.

Last but not least, detailed computer programs in Matlab are provided covering both type-1 and type-2 fuzzy logic systems. This aspect allows immediate applications to practical problems.

This book is greatly appreciated and highly recommended for anyone interested in type-1, or type-2 fuzzy logic systems aiming either to deeply understand these important areas or to apply them in his or her field of interest.

This book showed me how vector space methods are a natural way to use the computer for solving difficult problems in engineering design. Although my background in mathematics consists only of advanced calculus and linear algebra, I had no trouble following the theory and learning the applications. For electrical engineers, such as myself, the applications to digital filter design were particularly interesting. There are many other applications described, including the design of lenses, image processing, even neural nets. What I like is that the authors give the backgroud to each application so that even if you are unfamiliar with the field, there are clear and concise introductions that help your understanding of the problem. For example, in the digital filter design section, the authors explain why digital filters are important and how they relate to analog filters. To write a book like this takes not only broad knowledge but extraordinary expository skills.

Good things can come in small packages, and as the author I hope that this is true of my book. I have worked with virtual machines in the my classroom, professional life, and home environment for several years, and with this book I have been able to put a lot of what I have learned about the virtual computing environment into a compact resource that I hope you can use to get started with this exciting technology. The book covers the essential operation of 4 major virtualization products including VMware Workstation 6, VMware Server, Microsoft Virtual PC 2007, and Microsoft Virtual Server 2005. Covering all these products in one place makes this book very unique. In addition, with the exception of VMware Workstation 6, all of the products are available as free downloads. (There is also a 30-day trial version of VMware Workstation 6 that you can play with.) This book will allow you to experiment with each of these products, learning which best product suits your needs. You may ask, "How can you cover so many products in such a small book?" The reason is because we stick to the basics. In addition, there are a lot basic concepts that are similar to each of the products, providing opportunities to condense the material by eliminating redundancy. For example, Chapter 1 provides an overview of virtual machine technology and the capabilities found in most of today's products. In addition, Chapter 1 provides information on how to download each product from the product vendor's Website. Chapter 2 covers VMware Workstation 6, VMware Server, and VMware Player. Since these products share many common options and windows, I was able to keep the chapter to a minimum by eliminating repetition. Chapter 3 covers Microsoft's Virtual PC 2007 and Virtual Server 2005. Again, both products have many similar functions that do not need repetition. In addition to covering product installation and configuration, each chapter contains steps on how to perform many essential virtual machine tasks. The book also provides a number of End of Chapter Projects to challenge your abilities. In the near future I also plan to post step-by-step hands-on activities to my Web site that will allow the reader to apply each task covered in the book to a sample virtual machine application. I know that I am bias, but I sincerely hope you will try the book, and that you will find it one of the best buys on the computer book market.

Best wishes to all...

Ted Simpson - Author

Another book that Dr. L had us read. During the 2008 presidential debate I see one party is trying to buy votes even though the failures of socialism have been proven time and time again throughout history. This is the singular short work on the failures of socialism.

Friedrich Hayek's The Road to Serfdom was written at a time when the Labour Party of Britain was openly socialist. Although modern social democrats renounce the 's' word, socialism is indeed the root of their thinking, and in this exposition, his magnus opus, Hayek unabashedly sends socialism to the gutter where it belongs. Hayek's thesis, that socialism and totalitarianism are two birds of the same feather, has stood the test of time, and it continues to show up today in the cases of Venezuela or Bolivia. Hayek was arguably responsible for Labour's (and the Democrats') turn to the right, set in stone by former PM Tony Blair (and former President Clinton). This book is, however, still very relevant, exemplified by the Democrats' plan to invade the health care sector, the countless bureaucracies located in Washington, and President Bush's reckless invasion of privacy (which is related to Hayek's arguments about war time and peace time). Although Hayek often comes off as soft on a number of issues, he could not be nearly as dedicated as Milton Friedman to absolute freedom because the intelligentsia was on the far-left in the 40's.

For classical liberals, modern leftists, and conservatives alike, The Road to Serfdom is extraordinarily eye-opening.

The only, effective way to reject socialism is by attacking it's fundamental philosophical ideas. That collectivism is good and the individual must be sacrificed for the "good of the people". Attacking a philosophy such as communism or socialism, because it is not "practical" is a contradiction and undercuts any argument against such a corrupt philosophy. These ideas are not good in theory but bad in practice. They are evil in theory and therefore evil in practice.

I would like to also recommend Ayn Rand's, "The Virtue of Selfishness". This is THE work to understand Man's Individual Rights based on His Rational Nature. It is from these fundamental Truths that the ONLY proper function of a legitimate government is derived - The protection of Individual Rights.

Considering it my duty as an economics major, I took it upon myself to read this book, with little expectations as to its brilliance, and was completely swept away. Not only is Hayek extremely eloquent in articulating the case for free trade, he supports his arguments with facts (e.g. what was then going on in Nazi Germany) and with theory (e.g. why without even the historical evidence that we do have we must conclude that a centralized system cannot equally favor everyone).

Since it is my tendency to look at the 1 star reviews before making a 5 star one, I recognize that some people don't like Hayek because he doesn't recognize the great things about socialized medicine (like how a guy in Canada signed up for a CAT scan under his dog's name because animals are not covered under their highly efficient centralized health care...true story by the way) or the kind thoughts of socialist thinkers (please don't make me choose my selection of Marx quotes). But what Hayek does is present a prima facie case against socialism; before anyone can advocate socialism, they MUST address Hayek's arguments.

This is why I think before any socialist and libertarian face each other in a squabble, both must have read The Road to Serfdom so that they can hit on the applicable issues instead of babbling on about poverty statistics. Are you a socialist and disagree with Hayek? Fine, but read the book so that you know where your opponents stand. I really think that socialists think lovers of capitalism are greedy and have no ethics. But if you read our spokesman Hayek, you'll see why we think that the free market is actually BETTER for society.

Let's change the scope of the argument. Socialists should stop arguing about how some people are poor...yes, some people are poor...and demonstrate how a centralized system can make people BETTER than they would be under the free market system. How planning the systems of production would be more efficient and prosperous than under the system of competition. How giving all our freedoms to one entity would guarantee them for all. If you can effectively address these issues and the many more that Hayek brings up, we will soon see a blessed change in the current headache of debates on socialism.

Friedrich August von Hayek was an Austrian-British economist and political philosopher known for his defense of classical liberalism and free-market capitalism against socialist and collectivist thought in the mid-20th century. Since 1920s, he worked in Austria. Unwilling to return to Austria after its annexation to Nazi Germany, Hayek became a British citizen in 1938, a status he held for the remainder of his life. It was during this time that "The Road to Serfdom" originated, originally published by Routledge Press in March 1944 in the UK and then by the University of Chicago in September 1944.

Hayek's central thesis of this book is that all forms of collectivism lead logically and inevitably to tyranny, and he used the Soviet Union and Nazi Germany as examples of countries which had gone down "the road to serfdom" and reached tyranny.

The book has many worthy observations. For example, all people are different by their mental development (which is also influenced by family environment and education, not counting the physical differences of the brain and endocrine system) and thus the classes of the society are needed at least to give more developed people to fully put into action their potential. Liquidation of social classes will also liquidate the abilities of more developed individuals. The same is on the international level. Consider international planning. Whichever honest and democratically open panning system will be adopted, it will be opposed by less developed and poorer nations, because they will see it as ignorance or oppression of their interests. This is obvious - the needs and goals of poor or underdeveloped countries cannot match the goals of rich or developed countries; as the interests of more educated people cannot match the interests of less educated ones.

Many people came to a conclusion that the wealth, in some extent, depends on a level of education. The problem is that not all the people in equal extend incline to the education, to their self-improvement. This is because of the differences of their needs, habits, abilities, capabilities, and so on. Leo Tolstoy in his novel "Resurrection" arose a question of how to improve the level of education: from inside of each individual or from outside? Which came first, the chicken or the egg? Should first the level of education in the society be risen which yields a revolution (dialectic transition of quantity into quality) or the revolution should make the environment to foster the education. Hayek doesn't explicitly raise this issue, but brings parallel between delegation of decision making in managing an enterprise and managing the state. Hayek thought that if a company boss makes all decision making solely by himself and doesn't give the work (of decision making) back to the people (see Ronald Heifetz's publications), it is similar to the states with totalitarian government. Such a dictatorship, enterprise-wide or country-wide, can be used in particular circumstances, but should not be used in all cases as the absolutely correct way of management, according to Hayek.

This is a great book. I think the ideas here will change the world!

Excellent philosophy on the intelligence algorithm in mammals, neatly condensed into a computer-programmable structure.

My primary gripe is that the author blindly, almost pretentiously, assumes that all intelligence comes from within the brain, and that everything is contained and deterministic. I don't blame him, but he could have spent at least a sentence acknowledging the possibility of self-determinism, rather than poorly debunking it. I think there is still potential for a continuum of self-determinsim, regressing to this algorithm when fully or partially absent.

If nothing else, however, this book describes a good algorithm for the projection of true intelligence on the mechanical universe perceived by the author. Now, go make some smart robots!

Jeff Hawkins knows computer technology. He also knows the human brain, having studied it for much of his adult life. His personal fascination with human intelligence has become a passion for understanding. Very well written and enlightening.

This is an excellent and intriguiging book. I have been studying this topic quite a lot recently and the most interesting aspects were the realization of the internal structures of the cortex. Also, the idea that each of the senses just sends a "pattern stream" of information that can be interpreted by the brain using the same algorithm--no matter what type it is--was a revelation.
Some of his explanations of how the cortex layers, V1, V2, V4 and IT worked as a little hazy but perhaps I just dropped some connections.
For anyone wishing to learn in depth how the brain works, this book is a gotta read.

First the "facts":

Jeff Hawkings is not a scientist as many reviewers accurately point out. While he reviews some of the cutting edge approaches to artificial intelligence, his goal is definitely not to educate readers on these ideas, merely to give you an idea of the general mindset of these schools of thought.

In stark contrast to the "modern science", Hawkings spends most of the book discussing a novel and more holistic idea about the basic function of the brain and how this kind of perspective would influence the creation of Artificial Intelligence. The story is simple and compelling, a very stimulating and satisfying idea. While Hawkings does dive down into some very technical science as grounding points for his approach, he spends most of the book talking suggesting very accessible human behaviors and how they would be explained in his framework.

In this regard, the book is interesting and accessible to most readers. Advanced readers will find his more technical sections insightful and interesting, but not to the detriment of the casual reader.


Now the editorial:

To put it mildly, there's a reason why the greatest scientific minds seem to explode out of nowhere. The scientific establishment has a way of deciding that a certain thing is true and then using its systematic bureaucratic power to "box out" alternative ideas. This stifles variety and forces the most brilliant people (with right answers) to find unconventional channels for their insights.

After identifying the mainstream philosophies, Hawkins offers a paradigm shift in the approach to "intelligence". Instead of getting bogged down in the micro-advances of "modern science", he says, "What if the mind worked this other way?" He then carries this theme through diverse schools of thought, identifying both strong links to human behavior and existing science. While the details of his concept may not be quite right, the general concept has a simplicity and elegance both in the science and in how it can be seen in human nature. Even more amazing is the way that his simple premise explains so many things outside his "domain", a compelling test for new theories.

In my somewhat limited exposure to the subject, I suspect that the basic idea is so powerful that it (or something like it) will shatter the modern study of intelligence. At the same time, it is such a paradigm shift that the mainstream will no doubt ignore it for quite a while.

In that respect, this book offers a compelling and promising idea that is both accessible to an average reader and worth consideration by an expert in the field. This is a MUST READ for curious minds.

This is not my favorite machine learning book, but Tom Mitchell did us all a favor by writing it. It covers the breadth of topics that make up the machine learning discipline fairly completely. Since this book is about completely, there is also a shallowness, but that shallowness does not trim out complete descriptions of the algorithms covered. Oh no, all the gory math is there, what isn't there are simple examples.

My first time through the book, what gave me the biggest headache was trying to understand back propagation from the algorithm pseudo code and the proof of correctness. I really really wanted one simple example at that point to make sure I understood the correct use of all the greek terms.

So good book, but I really wanted "Machine Learning Examples" to go along with it back when I first picked it up. But once you understand, the book is a great reference.

The book machine learning by Mitchell provides a systematic overview of important concepts in the field. This is rather rare finding because most books present first of all algorithms but fall short communicating systematic insights that would help the reader to creatively develop methods by themselves.


It is needless to say that any book with the title 'machine learning' is inherent incomplete due to the incompletenss of the field itself. For this reason this book is not state of the art of current algrithms. Instead, again, concepts are at the center of focus.

Overall, well writen and a very good selection of examples and explanations. I recommend this to anyone for a general overview.

This book is a redaction of many different white papers on the topic of machine learning. The material is very credible and accepted in the field, with very little (if any) temporal information (short term at least). With that said, it is also very dry and academic, and requires a solid background in mathematics to understand. Even if you are in the field, you're likely to read certain pages several times to embrace a concept... but once you embrace it, you will have some of the best foundational knowledge there is on the subject. If you're in the machine-learning field, you'll benefit from revisiting some of these subject, and probably learn a new thing or two.

I read this book about 7 years ago while in the PhD program at Stanford University. I consider this book not only the best Machine Learning book, but one of the best books in all of Computer Science. It covers every branch of ML I know of and it covers it really well. I found Mitchell's chapter on Neural Networks more insightful than an entire book on NN's that I read. I also found his chapter on Reinforcement Learning more useful and better explained than an entire book on Reinforcement Learning that I also read. The other chapters cover other ML topics at the same level of quality and rigor.

The author did an amazing job in covering the breadth and depth of ML in less than 500 pages. I hope he will write a new edition to cover the advances that happened in the last decade.

I have used this book during my masters and found it to be an extremely helpful and a gentle introduction to the thick and things of machine learning applications. The various chapters are nicely paced with helpful problems at the end. Another great thing about the book is treatment of detailed examples with each concept and that the author carefully ties various concepts as they arise, with not just new, but also examples from previous chapters, which helps the user to understand different concepts applied to same problems thereby making clear difference between different methods. Also the author has a dedicated website with updated errata and notes, which is also very helpful! Having said that, I think the book is an introduction to various machine learning methods and one can easily follow on the references listed for detailed treatment of relevant topics.

A comprehensive textbook for undergraduate/graduate students. The book is complete with basic coverage to different types of networks and their working. Treatment to TCP and other Internet protocols well explained with pseudo code for different algorithms. Various extensions to TCP and network security basics well covered. The book does lack some mathematical treatment to the subject which could have improved the content.

This book is designed for someone who has prior knowledge with the underlying systems of a computer network. It is fairly in-depth, and explains a lot, but I think that for a true beginner it is hard to comprehend. I am working on a degree in Computer Science, so it wasn't like I was new to the idea of networking, but I found it hard to digest a lot of the technical aspects of this book. Some things could have been made to be more of an overview, rather than in-depth explanation of how something works at a bit level. I am now taking CCNA training, and some of the things talked about in this book do not relate at all to what someone needs to understand a working network.

I am studying this as part of my course. I have found this book teaches you from the base about Networking. It's really helpful in understanding the underlying technologies. Larry and Bruce have done a wonderful job in covering almost all of the aspects. But... I found only one thing lacking. That is...graphical explanations. This book is more of text with required graphics but in some places it took time to visualize the concept. I have had Behrouz A. Forozan's (I hope I have spelt the name correctly) "TCP/IP: The Complete Protocol Suite" along with this. Particularly I found some of the parts like ATM, Routing and other chapters' in this book textually gives us a thorough understanding and Forozan's book does depict that visually in a better way. Here is the bottomline, have this book as "The" texbook and have some other books like the as I mentioned before.

I've been dealing with computer networks in theory and practice for quite some time. This book is excellent reference and give in depth details and knowledge. The structure is well designed and the materials are following each other. If you are looking for a startup book though, this might not be the right choice.

Although this book clarifies the concepts of networking quite good, it lacks in-depth mathematical & statistical theory.
For those who are interested in the general picture, it's an excellent book. The authors actually succeeded quite good in not making it 'too dry'. For those who need 'more', like engineers and students in telecommunications/electronics, such as theories, applications and examples on efficiency, network performance parameters, etc., please look further as this book just doesn't offer that (and it probably never intended to).

If you're a .Net programmer, you might think an overview book like this is beneath you - it probably sounds like management oriented fluff. However, we all have to admit .Net is huge and constantly changing. It's tough to keep up on all its facets, let alone, put them into some sort of historical context. This book does that for you. What's more, it explains the why's and what-for's behind the various .Net pieces better than any other book I've run across. I think even experienced dotnetters will enjoy quite a few 'ah ha' moments reading this book.

If you're a management type, the first chapter alone is gold. Read it, learn it, and you'll probably be able to understand what your average dotnet programmer is saying. Best of all, when they call the Intermediate Language "bytecode" (which I've noticed happens pretty often) you get to correct them: "bytecode is Java - in dotnet we call it IL." Surely this moment of triumph alone is worth the price of the book.

One criticism: Why a lion on the cover? I think it makes this book look like one of those learn how to program X in 30 days books, that is, a cheap poorly written ripoff. This book deserves better! I'd put it in the same class as a great introductory text book, so why not make it look like one.

I'm a software engineer who has a background with C++ and Java. I normally stick up my nose at anything Microsoft. So I was skeptical picking up this book, but I'm glad I did. It presents the user with an overall look at the .NET framework.

This book is good for people with a Java background.

It probably wouldn't be a good book for the avid .NET user.

Excellent coverage - I learnt a lot and the examples are very good as well.

While this book is well written and provides an overview, it is important to note that there is absolutely no tutorial. If you are expecting an instructional section that is set up as a series of steps that progress through levels of difficulty and understanding that provide a working knowlege of the subject (as is implied by the word tutorial in the title)you will not find that in this book.

Good overview of the .NET technology. Although it is a bit out of date...