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comp.ai.neural-nets FAQ, Part 2 of 7: Learning

( Part1 - Part2 - Part3 - Part4 - Part5 - Part6 - Part7 - Single Page )
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See reader questions & answers on this topic! - Help others by sharing your knowledge
Copyright 1997, 1998, 1999, 2000, 2001, 2002 by Warren S. Sarle, Cary, NC,
USA. Answers provided by other authors as cited below are copyrighted by
those authors, who by submitting the answers for the FAQ give permission for
the answer to be reproduced as part of the FAQ in any of the ways specified
in part 1 of the FAQ. 

This is part 2 (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

   What are combination, activation, error, and objective functions?
      Combination functions
      Activation functions
      Error functions
      Objective functions
   What are batch, incremental, on-line, off-line, deterministic,
   stochastic, adaptive, instantaneous, pattern, epoch, constructive, and
   sequential learning?
      Batch vs. Incremental Learning (also Instantaneous, Pattern, and
      Epoch)
      On-line vs. Off-line Learning
      Deterministic, Stochastic, and Adaptive Learning
      Constructive Learning (Growing networks)
      Sequential Learning, Catastrophic Interference, and the
      Stability-Plasticity Dilemma
   What is backprop?
   What learning rate should be used for backprop?
   What are conjugate gradients, Levenberg-Marquardt, etc.?
   How does ill-conditioning affect NN training?
   How should categories be encoded?
   Why not code binary inputs as 0 and 1?
   Why use a bias/threshold?
   Why use activation functions?
   How to avoid overflow in the logistic function?
   What is a softmax activation function?
   What is the curse of dimensionality?
   How do MLPs compare with RBFs?
      Hybrid training and the curse of dimensionality
      Additive inputs
      Redundant inputs
      Irrelevant inputs
   What are OLS and subset/stepwise regression?
   Should I normalize/standardize/rescale the data?
      Should I standardize the input variables?
      Should I standardize the target variables?
      Should I standardize the variables for unsupervised learning?
      Should I standardize the input cases?
   Should I nonlinearly transform the data?
   How to measure importance of inputs?
   What is ART?
   What is PNN?
   What is GRNN?
   What does unsupervised learning learn?
   Help! My NN won't learn! What should I do?

Part 3: Generalization
Part 4: Books, data, etc.
Part 5: Free software
Part 6: Commercial software
Part 7: Hardware and miscellaneous

User Contributions:

1
Majid Maqbool
Sep 27, 2024 @ 5:05 am
https://techpassion.co.uk/how-does-a-smart-tv-work-read-complete-details/
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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Last Update March 27 2014 @ 02:11 PM