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Copyright 1997, 1998, 1999, 2000, 2001, 2002 by Warren S. Sarle, Cary, NC, USA. Reviews provided by other authors as cited below are copyrighted by those authors, who by submitting the reviews for the FAQ give permission for the review to be reproduced as part of the FAQ in any of the ways specified in part 1 of the FAQ. This is part 4 (of 7) of a monthly posting to the Usenet newsgroup comp.ai.neural-nets. See the part 1 of this posting for full information what it is all about. ========== Questions ========== ******************************** Part 1: Introduction Part 2: Learning Part 3: Generalization Part 4: Books, data, etc. Books and articles about Neural Networks? The Best The best of the best The best popular introduction to NNs The best introductory book for business executives The best elementary textbooks The best books on using and programming NNs The best intermediate textbooks on NNs The best advanced textbook covering NNs The best book on neurofuzzy systems The best comparison of NNs with other classification methods Other notable books Introductory Bayesian learning Biological learning and neurophysiology Collections Combining networks Connectionism Feedforward networks Fuzzy logic and neurofuzzy systems General (including SVMs and Fuzzy Logic) History Knowledge, rules, and expert systems Learning theory Object oriented programming On-line and incremental learning Optimization Pulsed/Spiking networks Recurrent Reinforcement learning Speech recognition Statistics Time-series forecasting Unsupervised learning Books for the Beginner Not-quite-so-introductory Literature Books with Source Code (C, C++) The Worst Journals and magazines about Neural Networks? Conferences and Workshops on Neural Networks? Neural Network Associations? Mailing lists, BBS, CD-ROM? How to benchmark learning methods? Databases for experimentation with NNs? UCI machine learning database UCI KDD Archive The neural-bench Benchmark collection Proben1 Delve: Data for Evaluating Learning in Valid Experiments Bilkent University Function Approximation Repository NIST special databases of the National Institute Of Standards And Technology: CEDAR CD-ROM 1: Database of Handwritten Cities, States, ZIP Codes, Digits, and Alphabetic Characters AI-CD-ROM Time series Financial data USENIX Faces Linguistic Data Consortium Otago Speech Corpus Astronomical Time Series Miscellaneous Images StatLib Part 5: Free software Part 6: Commercial software Part 7: Hardware and miscellaneous
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PDP++ is a neural-network simulation system written in C++, developed as an advanced version of the original PDP software from McClelland and Rumelhart's "Explorations in Parallel Distributed Processing Handbook" (1987). The software is designed for both novice users and researchers, providing flexibility and power in cognitive neuroscience studies. Featured in Randall C. O'Reilly and Yuko Munakata's "Computational Explorations in Cognitive Neuroscience" (2000), PDP++ supports a wide range of algorithms. These include feedforward and recurrent error backpropagation, with continuous and real-time models such as Almeida-Pineda. It also incorporates constraint satisfaction algorithms like Boltzmann Machines, Hopfield networks, and mean-field networks, as well as self-organizing learning algorithms, including Self-organizing Maps (SOM) and Hebbian learning. Additionally, it supports mixtures-of-experts models and the Leabra algorithm, which combines error-driven and Hebbian learning with k-Winners-Take-All inhibitory competition. PDP++ is a comprehensive tool for exploring neural network models in cognitive neuroscience.
PDP++ is a neural-network simulation system written in C++, developed as an advanced version of the original PDP software from McClelland and Rumelhart's "Explorations in Parallel Distributed Processing Handbook" (1987). The software is designed for both novice users and researchers, providing flexibility and power in cognitive neuroscience studies. Featured in Randall C. O'Reilly and Yuko Munakata's "Computational Explorations in Cognitive Neuroscience" (2000), PDP++ supports a wide range of algorithms. These include feedforward and recurrent error backpropagation, with continuous and real-time models such as Almeida-Pineda. It also incorporates constraint satisfaction algorithms like Boltzmann Machines, Hopfield networks, and mean-field networks, as well as self-organizing learning algorithms, including Self-organizing Maps (SOM) and Hebbian learning. Additionally, it supports mixtures-of-experts models and the Leabra algorithm, which combines error-driven and Hebbian learning with k-Winners-Take-All inhibitory competition. PDP++ is a comprehensive tool for exploring neural network models in cognitive neuroscience.
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Section Contents
- News Headers
- Books and articles about Neural Networks?
- Journals and magazines about Neural Networks?
- Conferences and Workshops on Neural
- Neural Network Associations?
- Mailing lists, BBS, CD-ROM?
- How to benchmark learning methods?
- Databases for experimentation with NNs?
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