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Connectionism A Hands-on Approach [Paperback]

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  • Category: Books (Psychology)
  • Author:  Dawson, Michael R. W.
  • Author:  Dawson, Michael R. W.
  • ISBN-10:  1405128070
  • ISBN-10:  1405128070
  • ISBN-13:  9781405128070
  • ISBN-13:  9781405128070
  • Publisher:  Wiley-Blackwell
  • Publisher:  Wiley-Blackwell
  • Pages:  208
  • Pages:  208
  • Binding:  Paperback
  • Binding:  Paperback
  • Pub Date:  01-May-2005
  • Pub Date:  01-May-2005
  • SKU:  1405128070-11-MPOD
  • SKU:  1405128070-11-MPOD
  • Item ID: 100745311
  • List Price: $64.50
  • Seller: ShopSpell
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  • Delivery by: Jan 19 to Jan 21
  • Notes: Brand New Book. Order Now.
Connectionism is a “hands on” introduction to connectionist modeling through practical exercises in different types of connectionist architectures.
  • explores three different types of connectionist architectures – distributed associative memory, perceptron, and multilayer perceptron
  • provides a brief overview of each architecture, a detailed introduction on how to use a program to explore this network, and a series of practical exercises that are designed to highlight the advantages, and disadvantages, of each
  • accompanied by a website at http://www.bcp.psych.ualberta.ca/~mike/Book3/ that includes practice exercises and software, as well as the files and blank exercise sheets required for performing the exercises
  • designed to be used as a stand-alone volume or alongside Minds and Machines: Connectionism and Psychological Modeling (by Michael R.W. Dawson, Blackwell 2004)
1. Hands-on Connectionism.

1.1 Connectionism In Principle And In Practice.

1.2 The Organization Of This Book.

2. The Distributed Associative Memory.

2.1 The Paired Associates Task.

2.2 The Standard Pattern Associator.

2.3 Exploring The Distributed associative memory.

3. The James Program.

3.1 Introduction.

3.2 Installing The Program.

3.3 Teaching A Distributed Memory.

3.4 Testing What The Memory Has Learned.

3.5 Using The Program.

4. Introducing Hebb Learning.

4.1 Overview Of The Exercises.

4.2 Hebb Learning Of Basis Vectol£?

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