Artificial Intelligence Books

This is an excellent book collection of articles on CMC. It's especially a great resource for historical research into the early days of the Internet and the web. Although some topics may seem somewhat dated, nevertheless they are still applicable to the present date in terms of the nature of network mediated communications.

The main focus of the book collection is on textual communications. It also expands on CMC's impacts on knowledge creation and sharing. The theoretical works in the last two chapters further expand the discussions on the nature of virtual world and virtual interaction.

The book is an essential resource for people interested in surveying the research in CMC, computer-supported cooperative work, and net-based communications

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

How can any publisher announce the publication with a title and everything else for 1999!

If you've ever read Professor Grossberg's theories, you might have noticed most of them seem to come from a couple of very interesting basic principles and ideas. In this book you can find the basis of some of his theories, as always very well orginized and very enjoyable.

This book is ideal to be used by practicing design engineers involved with applications of neural networks in industry and academia.

The book contains a collection of selected conference papers from IEEE. I am an Industrial practitioner of Control Systems, and in this area the book includes applications of neural networks in:
- Process Control.
- Electric Motors Control.
- Robotic Control.
- Structure control.
- Vehicle Control.
- Voltaje Control.

For individuals interested in other fields, the books also cover the following areas of application:
- Power Systems (Fault detection, , load forecasting, security assestment, etc.)
- Medical Systems (Diagnosis, signal processing, and image processing).
- Information Processing (Character recognition, financial processing, information retrieval, and natural language).
- Signal Processing (Comunications, remote sensing, seismic signal processing, and speech processing).
- Manufacturing, Production, and Inspection (Automatic inspection, management, manufacturing, and software production).
- Vehicles (Robot navigation, and automobiles).

This book provides a detailed, yet accessible technical explanation of the inner workings of backpropagation of errors, the most commonly used training method for feedforward neural networks.

This book is for the programmer who wishes to implement backprop, or for the data miner who wishes to understand backprop. This book is not well-suited for people without a technical (math) background.

Pros:
-Pseudocode is provided
-Explanation advances from plain vanilla backprop to variants including enhancements like adaptive step size and momentum
-Application of neural networks is covered, including practical issues such as appropriate data representation and avoidance of over-fitting
-A few alternative architectures are explained (briefly) at the end of the book

Cons:
-This book was published in 1996, so it is getting a little old, though it has held up well

While others introduce artificial neural networks (ANNs) from a psychological perspective, and others do so as an electrical engineering book, this reference describes ANNs more from an artificial intelligence (AI) point of view, and may be more appropriate for the reader with some background in computer science.

This is a very good book. It provides a nice, clearly presented introduction to neural networks both in theory and applications. The basic maths are easy to understand and the list of references is very complete.

The book explains and deduces mathematically the working principle of many architectures, and presents commented examples (good ones) in structured English (very similar to Pascal). I used it as the basis for many NN's and felt quite satisfied in all cases.

This book have the basics of neural networks (NN) treated at undergraduate level adequated to students of physics, mathematics or engineering. The relevant concepts are all explained and it recquires no previous knowledge in the area from the reader. It is a perfect introduction to the theory of neural networks for those who are iniciating in the field and have no experience with NN.