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📙 Connectionism: A Hands-On Approach by Michael R. W. Dawson(auth.) — free pdf


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)
  • Content:
    Chapter 1 Hands?On Connectionism (pages 1–4):
    Chapter 2 The Distributed Associative Memory (pages 5–8):
    Chapter 3 The James Program (pages 9–21):
    Chapter 4 Introducing Hebb Learning (pages 22–29):
    Chapter 5 Limitations of Hebb Learning (pages 30–36):
    Chapter 6 Introducing the Delta Rule (pages 38–40):
    Chapter 7 Distributed Networks and Human Memory (pages 41–45):
    Chapter 8 Limitations of Delta Rule Learning (pages 46–47):
    Chapter 9 The Perceptron (pages 49–57):
    Chapter 10 The Rosenblatt Program (pages 58–71):
    Chapter 11 Perceptrons and Logic Gates (pages 72–80):
    Chapter 12 Performing More Logic with Perceptrons (pages 81–85):
    Chapter 13 Value Units and Linear Nonseparability (pages 87–90):
    Chapter 14 Network by Problem Type Interactions (pages 91–93):
    Chapter 15 Perceptrons and Generalization (pages 94–98):
    Chapter 16 Animal Learning Theory and Perceptrons (pages 99–107):
    Chapter 17 The Multilayer Perceptron (pages 108–113):
    Chapter 18 The Rumelhart Program (pages 114–128):
    Chapter 19 Beyond the Perceptron's Limits (pages 129–132):
    Chapter 20 Symmetry as a Second Case Study (pages 133–136):
    Chapter 21 How Many Hidden Units? (pages 137–144):
    Chapter 22 Scaling Up with the Parity Problem (pages 145–150):
    Chapter 23 Selectionism and Parity (pages 151–156):
    Chapter 24 Interpreting a Small Network (pages 157–162):
    Chapter 25 Interpreting Networks of Value Units (pages 163–173):
    Chapter 26 Interpreting Distributed Representations (pages 174–182):
    Chapter 27 Creating Your Own Training Sets (pages 183–187):

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