{"product_id":"connectionism-isbn-9781405128070","title":"Connectionism","description":"\u003cp\u003e\u003cb\u003e\u003ci\u003eConnectionism\u003c\/i\u003e is a \"hands on\" introduction to connectionist modeling through practical exercises in different types of connectionist architectures.\u003c\/b\u003e\u003c\/p\u003e \u003cul\u003e \u003cli\u003eexplores three different types of connectionist architectures – distributed associative memory, perceptron, and multilayer perceptron\u003c\/li\u003e \u003cli\u003eprovides 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\u003c\/li\u003e \u003cli\u003eaccompanied by a website at \u003cb\u003ehttp:\/\/www.bcp.psych.ualberta.ca\/~mike\/Book3\/\u003c\/b\u003e that includes practice exercises and software, as well as the files and blank exercise sheets required for performing the exercises\u003c\/li\u003e \u003cli\u003edesigned to be used as a stand-alone volume or alongside \u003ci\u003eMinds and Machines: Connectionism and Psychological Modeling\u003c\/i\u003e (by Michael R.W. Dawson, Blackwell 2004)\u003c\/li\u003e \u003c\/ul\u003e  \u003cb\u003e1. Hands-on Connectionism\u003c\/b\u003e. \u003cp\u003e1.1 Connectionism In Principle And In Practice.\u003c\/p\u003e \u003cp\u003e1.2 The Organization Of This Book.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2. The Distributed Associative Memory.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 The Paired Associates Task.\u003c\/p\u003e \u003cp\u003e2.2 The Standard Pattern Associator.\u003c\/p\u003e \u003cp\u003e2.3 Exploring The Distributed associative memory.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3. The James Program.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction.\u003c\/p\u003e \u003cp\u003e3.2 Installing The Program.\u003c\/p\u003e \u003cp\u003e3.3 Teaching A Distributed Memory.\u003c\/p\u003e \u003cp\u003e3.4 Testing What The Memory Has Learned.\u003c\/p\u003e \u003cp\u003e3.5 Using The Program.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4. Introducing Hebb Learning.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Overview Of The Exercises.\u003c\/p\u003e \u003cp\u003e4.2 Hebb Learning Of Basis Vectors.\u003c\/p\u003e \u003cp\u003e4.3 Hebb Learning Of Orthonormal, Non-Basis Vectors.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5. Limitations Of Hebb Learning\u003c\/b\u003e.\u003c\/p\u003e \u003cp\u003e5.1 Introduction.\u003c\/p\u003e \u003cp\u003e5.2 The Effect Of Repetition.\u003c\/p\u003e \u003cp\u003e5.3 The Effect Of Correlation.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6. Introducing The Delta Rule.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction.\u003c\/p\u003e \u003cp\u003e6.2 The Delta Rule.\u003c\/p\u003e \u003cp\u003e6.3 The Delta Rule And The Effect Of Repetition.\u003c\/p\u003e \u003cp\u003e6.4 The Delta Rule And The Effect Of Correlation.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7. Distributed Networks And Human Memory.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Background On The Paired Associate Paradigm.\u003c\/p\u003e \u003cp\u003e7.2 The Effect Of Similarity On The Distributed Associative Memory.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8. Limitations Of Delta Rule Learning.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction.\u003c\/p\u003e \u003cp\u003e8.2 The Delta Rule And Linear Dependency.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9. The Perceptron.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction.\u003c\/p\u003e \u003cp\u003e9.2 The Limits Of Distributed Associative Memories, And Beyond.\u003c\/p\u003e \u003cp\u003e9.3 Properties Of The Perceptron.\u003c\/p\u003e \u003cp\u003e9.4 What Comes Next.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10. The Rosenblatt Program.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction.\u003c\/p\u003e \u003cp\u003e10.2 Installing The Program.\u003c\/p\u003e \u003cp\u003e10.3 Training A Perceptron.\u003c\/p\u003e \u003cp\u003e10.4 Testing What The Memory Has Learned.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11. Perceptrons And Logic Gates.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction.\u003c\/p\u003e \u003cp\u003e11.2 Boolean Algebra.\u003c\/p\u003e \u003cp\u003e11.3 Perceptrons And Two-Valued Algebra.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12. Performing More Logic With Perceptrons.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Two-Valued Algebra And Pattern Spaces.\u003c\/p\u003e \u003cp\u003e12.2 Perceptrons And Linear Separability.\u003c\/p\u003e \u003cp\u003e12.3 Appendix Concerning The DawsonJots Font.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13. Value Units And Linear Nonseparability.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Linear Separability And Its Implications.\u003c\/p\u003e \u003cp\u003e13.2 Value Units And The Exclusive-Or Relation.\u003c\/p\u003e \u003cp\u003e13.3 Value Units And Connectedness.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14. Network By Problem Type Interactions.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 All Networks Were Not Created Equally.\u003c\/p\u003e \u003cp\u003e14.2 Value Units And The Two-Valued Algebra.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15. Perceptrons And Generalization.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Background.\u003c\/p\u003e \u003cp\u003e15.2 Generalization And Savings For The 9-Majority Problem.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16. Animal Learning Theory And Perceptrons.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16.1 Discrimination Learning.\u003c\/p\u003e \u003cp\u003e16.2 Linearly Separable Versions Of Patterning.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17. The Multilayer Perceptron.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e17.1 Creating Sequences Of Logical Operations.\u003c\/p\u003e \u003cp\u003e17.2 Multilayer Perceptrons And The Credit Assignment Problem.\u003c\/p\u003e \u003cp\u003e17.3 The Implications Of The Generalized Delta Rule.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18. The Rumelhart Program.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction.\u003c\/p\u003e \u003cp\u003e18.2 Installing The Program.\u003c\/p\u003e \u003cp\u003e18.3 Training A Multilayer Perceptron.\u003c\/p\u003e \u003cp\u003e18.4 Testing What The Network Has Learned.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19. Beyond The Perceptron’s Limits.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction.\u003c\/p\u003e \u003cp\u003e19.2 The Generalized Delta Rule And Exclusive-Or.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20. Symmetry As A Second Case Study.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e20.1 Background.\u003c\/p\u003e \u003cp\u003e20.2 Solving Symmetry Problems With Multilayer Perceptrons.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21. How Many Hidden Units?\u003c\/b\u003e.\u003c\/p\u003e \u003cp\u003e21.1 Background.\u003c\/p\u003e \u003cp\u003e21.2 How Many Hidden Value Units Are Required For 5-Bit Parity?.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e22. Scaling Up With The Parity Problem.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e22.1 Overview Of The Exercises.\u003c\/p\u003e \u003cp\u003e22.2 Background.\u003c\/p\u003e \u003cp\u003e22.3 Exploring The Parity Problem.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e23. Selectionism And Parity.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e23.1 Background.\u003c\/p\u003e \u003cp\u003e23.2 From Connectionism To Selectionism.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e24. Interpreting A Small Network.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e24.1 Background.\u003c\/p\u003e \u003cp\u003e24.2 A Small Network.\u003c\/p\u003e \u003cp\u003e24.3 Interpreting This Small Network.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e25. Interpreting Networks Of Value Units.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e25.1 Background.\u003c\/p\u003e \u003cp\u003e25.2 Banding In The First Monks Problem.\u003c\/p\u003e \u003cp\u003e25.3 Definite Features In The First Monks Problem.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e26. Interpreting Distributed Representations.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e26.1 Background.\u003c\/p\u003e \u003cp\u003e26.2 Interpreting A 5-Parity Network.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e27. Creating Your Own Training Sets.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e27.1 Background.\u003c\/p\u003e \u003cp\u003e27.2 Designing And Building A Training Set.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e  “This is a first-rate textbook, Enabling readers to perform simulations described, it provides a very user-friendly introduction to the essential material, which it sets in an engaging, historically informed context.” \u003ci\u003eAnne Jaap Jacobson, University of Houston\u003c\/i\u003e \u003cb\u003eMichael R. W. Dawson \u003c\/b\u003eis a member of the Department of Psychology and the Biological Computation Project at the University of Alberta, Canada. He is the author of \u003ci\u003eUnderstanding Cognitive Science \u003c\/i\u003e(Blackwell , 1998) and \u003ci\u003eMinds and Machines\u003c\/i\u003e (Blackwell, 2004). \u003cp\u003e\u003cb\u003eCONNNECTIONISM\u003c\/b\u003e is a “hands on” introduction to connectionist modeling. Three different types of connectionist architectures – distributed associative memory, perceptron, and multilayer perceptron – are explored. In an accessible style, Dawson 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 and to provide a \"road map\" to the field of cognitive modeling.\u003c\/p\u003e \u003cp\u003eThis book is designed to be used as a stand-alone volume, or alongside \u003ci\u003eMinds and Machines: Connectionism and Psychological Modeling\u003c\/i\u003e (Blackwell Publishing, 2004). An accompanying website is available at \u003cb\u003ewww.bcp.psych.ualberta.ca\/%7emike\/book3\/index.html\u003c\/b\u003e and includes practice exercises and software, as well as the files and blank exercise sheets that are required for performing the exercises.\u003c\/p\u003e","brand":"Wiley-Blackwell","offers":[{"title":"Default Title","offer_id":47988975141093,"sku":"NP9781405128070","price":57.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781405128070.jpg?v=1761782278","url":"https:\/\/k12savings.com\/products\/connectionism-isbn-9781405128070","provider":"K12savings","version":"1.0","type":"link"}