Neural Networks Books

Dr. Joaquin Fuster, in Memory in the Cerebral Cortex : An Empirical Approach to Neural Networks in the Human and Nonhuman Primate, has organized and integrated a wealth of neurobiological and neuropsychological information concerning cortical memory processing and formation. He provides the reader with a clear and succinct understanding of how the cerebral cortex processes information for ultimate storage into memory. The book starts with an overview of cortical specialization of function and then examines developmental issues and memory formation, different models for understanding memory networks, provides a detailed, but concise, description of perceptual and motor memory organization, examines the dynamics of cortical memory retrieval and attention, as well as the phenomenology of memory. In response to this book's thorough and concise presentation, I highly recommend its review by the many professionals who study and work with cortical memory.

This is a very good introductory book on mobile robots. It assumes no background of the readers in the subject. Chapter 3 provides a introduction to the hardwared aspects including sensors and actuators. Chapter 4 on machine learning by robots is also very very interesting. The case studies presented in chapter 5 and 6 will be very much useful to the new-entrants of this discipline. Chapter 7 on analysis of robot behaviour is important for the researchers working in the discipline.I like the book for its simplicity in presentation and direct reference to the points to be discussed, rather than providing a lengthy introduction to the topics. The phrase "A practcical introduction" in the title is really worthwhile for the book.

What distinguishes this book from other neuronal modeling books? Firstly, it contains a solid quantitative flavor necessary for real advances in neural networks. Secondly, it places an emphasis on neuronal models not encountered in computational neuroscience. Thirdly, it is written with authority by some well known names in the field. Each chapter is self-contained and written in a didactic fashion with a future perspectives section and a problem set. The contents range from stochastic fluctuations of ionic channels to morphologically realistic neural networks. Some features include: Lanczos matrices to construct fully equivalent cables, application of Maxwell equations to derive the cable equation, and a chapter devoted to nonlinear cable theory co-authored by one of the founders of theoretical biophysics. This book is thoroughly recommended to anyone who is serious about neuronal modeling.

This book excels at presenting the current state of this exciting new field of scientific endeavor. I thought the list of references alone was worth the price of the book.

What it is: a consensus among top scientists on what Network Science is all about and a road map showing what the U.S. Government in general and the Department of the Army in particular should do to stimulate its development.

What is isn't: an in-depth exploration of any of the facets of Network Science. If you are eager to dive into the nitty-gritty, however, this book includes a phenomenal list of references.

This book is a great introduction to the subject of Neural Networks for someone with no background in the field. The book clearly presents a wide range of techniques for neural computing with sections dedicated to the background of the technique, algorithms for implementing the technique, the mathematics behind the algorithms, and examples where such networks have been used. All of the challenging mathematics are set aside in clearly marked sections which can be skipped by those not interested in the details. The language of the book is intentionally accessible, and the authors carefully indicate where neural networks are like and unlike real brain activity.

Each chapter has a bibliography indicating further references. Many are the actualy primary sources written by those who researched the field or technique discussed. For one instance, when the authors had changed terminology from a source for clarity, they even explained the words used by the original author to fascilitate studying the original sources.

For someone who has heard of neural networks but lacks a full understanding of what they do and how they work, this book is an excellent choice. After every chapter I was filled with a longing to go write a neural network to play with.

A stonking blockbuster of a book, filled with raw power and suspense! From the heart stopping narrative on the on the Need for Conditions on the Activation Functions, to the torrid account of Classes of Finite Pseudo-dimension, this is truly the most exhilarating and disturbing description yet of the mathematical foundations of machine learning. I eagerly await the sequel.

This book is an excellent introduction to neural networks for the reader with a good mathematics background. The first few chapters describe popular and useful algorithms for implementing neural networks (Hopfield, backpropagation). Much of the book is devoted to the theory behind these networks (capacity, convergence and the dynamical systems approach, Markov chains). [This gave me, a total novice before reading the book, a much better idea of how these networks function, so much so that I can implement them in software.] Complementing the theory are numerous stimulating projects that illustrate many applications and open questions in the field.

De Wilde's book is very clearly written. Its compact size is a joy in this age of mammoth, but often poorly-written and substanceless, textbooks.

A good introduction to neural nets and their applicability tothe futures markets. Provides mathematical/theoretical basis fortechniques involved in neural net training, testing, chaos and touches on nonlinear systems in general. Also provides interesting benchmarks of several nets against several time series. Great bibliography. Includes Fortran source code for running trained nets but not for training a net.

Currently many people know how to 'use' Neural Networks, but very rare who know deeply what is going on behind. This is because they overlook the basic concepts at their first learning of a certain Neural Network. On the other hand, there are still not many books with the intention to build a firm understanding of the fundamental concepts. Instead of 'begin' with the basic, many books eagerly rammed down your brain with all the 'hoopla' of fast proliferation in Neural Networks community and leave you in preplexity (If you read Haykin's Comprehensive Foundations as your first reading aims at understand the basic, you will soon know what I mean). In such condition, how could you even be able to develop your own simple Neural Networks ?

Before you start get in deep with any kinds of Neural Networks widely used today (backpropagation, rbf, Hopfield, Boltzman Machine, probabilistic neural net, etc) you can try this book at first. After knowing the basic mechanism, you could savely learn from some advance books (or papers), and so far I think 'Introduction to Neural Computations by John Hertz et.al.' is still the best though a bit outdate.

This book give me a lucid presentation about the basic 'mechanism' behind Neural Networks.
After I read this book, I could only give it five stars :-).

This is a short but very clear and very detailed book. It gives most of the techniques and the concepts to understand neural networks. it was my first introduction to the topic and since then I have been able to read more advanced texts and papers. I would say that this is the ideal entry point to the topic as well as an interesting book for the general reader willing to have a general overview of the field