| Neural networks at your fingertips
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The programs on this site deal with both these issues. You will find: ready-to-reuse software simulators for eight of the most popular neural network architectures, coded in portable, self-contained ANSwith complete example applications from a variety of well-known application domains.
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Neural Networks FAQ
Part 1 Introduction
Part 2 Introduction
Part 3 Generalization
Part 4 Books, Data etc
Part 5 Free Software
Part 6 Commercial Software
Part 7 Hardware and miscellaneous
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These are the seven parts of a monthly posting to the Usenet newsgroup comp.ai.neural-nets (as well as comp.answers and news.answers, where it should be findable at any time). Its purpose is to provide basic information for individuals who are new to the field of neural networks or who are just beginning to read this group. It will help to avoid lengthy discussion of questions that often arise for beginners. |
Artificial Intelligence FAQ
Part 1 General Questions & Answers
Part 2 Newsgroups and Mailing Lists
Part 3 Associations and Journals
Part 4 Bibliography
Part 5 AI Web Directories & Online Papers
Part 6 AI Software
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Certain questions and topics come up frequently in the various network discussion groups devoted to and related to Artificial Intelligence (AI). This article is an attempt to gather these questions and their answers into a convenient reference for AI researchers. It is posted on a monthly basis. The hope is that this will cut down on the user time and network bandwidth used to post, read and respond to the same questions over and over, as well as providing education by answering questions some readers may not even have thought to ask. |
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| Databases and AI: Artificial Intelligence Segment
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Lecture Notes
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| Artifical Intelligence II
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Lecture Notes
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| Introduction to Machine Learning
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The notes survey many of the important topics in machine learning circa 1996. My intention was to pursue a middle ground between theory and practice. The notes concentrate on the important ideas in machine learning. The goal was to give the reader sufficient preparation to make the extensive literature on machine learning accessible.
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| Machine Learning,Neural & Statistical Classification
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by D. Michie, D.J. Spiegelhalter, C.C. Taylor
This book is based on the EC (ESPRIT) project StatLog which compare and evaluated a range of classification techniques, with an assessment of their merits, disadvantages and range of application. This integrated volume provides a concise introduction to each method, and reviews comparative trials in large-scale commercial and industrial problems. It makes accessible to a wide range of workers the complex issue of classification as approached through machine learning, statistics and neural networks, encouraging a cross-fertilization between these discplines.
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