Neural Networks: An Introduction (Physics of Neural Networks Series) Book

Neural Networks: An Introduction (Physics of Neural Networks Series), Neural Networks presents concepts of neural-network models and techniques of parallel distributed processing in a three-step approach: - A brief overview of the neural structure of the brain and the history of neural-network modeling introduces to , Neural Networks: An Introduction (Physics of Neural Networks Series)

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Neural Networks: An Introduction (Physics of Neural Networks Series)
Written by author B. Muller
Published by Springer-Verlag New York, LLC, September 2002
Neural Networks presents concepts of neural-network models and techniques of parallel distributed processing in a three-step approach: - A brief overview of the neural structure of the brain and the history of neural-network modeling introduces to
Neural Networks presents concepts of neural-network models and techniques of parallel distributed processing in a three-step approach: - A brief overview of the neural structure of the brain and the history of neural-network modeling introduces

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1 The Structure of the Central Nervous System 3
2 Neural Networks Introduced 13
3 Associative Memory 24
4 Stochastic Neurons 38
5 Cybernetic Networks 46
6 Multilayered Perceptrons 52
7 Applications 63
8 More Applications of Neural Networks 72
9 Network Architecture and Generalization 93
10 Associative Memory: Advanced Learning Strategies 108
11 Combinatorial Optimization 126
12 VLSI and Neural Networks 135
13 Symmetrical Networks with Hidden Neurons 144
14 Coupled Neural Networks 151
15 Unsupervised Learning 162
16 Evolutionary Algorithms for Learning 174
17 Statistical Physics and Spin Glasses 191
18 The Hopfield Network for [actual symbol not reproducible] 201
19 The Hopfield Network for Finite p/N 209
20 The Space of Interactions in Neural Networks 231
21 Numerical Demonstrations 249
22 ASSO: Associative Memory 253
23 ASSCOUNT: Associative Memory for Time Sequences 264
24 PERBOOL: Learning Boolean Functions with Back-Prop 268
25 PERFUNC: Learning Continuous Functions with Back-Prop 275
26 Solution of the Traveling-Salesman Problem 279
27 KOHOMAP: The Kohonen Self-organizing Map 291
28 BTT: Back-Propagation Through Time 296
29 Neurogen: Using Genetic Algorithms to Train Networks 303
References 307
Index 325

Title: Neural Networks: An Introduction (Physics of Neural Networks Series)

Manufacturer:

Steidl Publishing

Item Number: 9783540602071

Publication Date: September 2002

Number: 2

Product Description: Full Name: Neural Networks: An Introduction (Physics of Neural Networks Series); Short Name:Neural Networks: An Introduction

Universal Product Code (UPC): 9783540602071

WonderClub Stock Keeping Unit (WSKU): 9783540602071

Rating: 3/5 based on 2 Reviews

Image Location: https://wonderclub.com/images/covers/20/71/9783540602071.jpg

Category: Media >> Books

Weight: 0.200 kg (0.44 lbs)

Width: 6.220 cm (2.45 inches)

Heigh : 9.280 cm (3.65 inches)

Depth: 0.900 cm (0.35 inches)

Date Added: August 25, 2020, Added By: Ross

Date Last Edited: August 25, 2020, Edited By: Ross

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$99.99	Digital	Arrives between May 13 - Jun 12 Login to your account for a direct download. Some files are emailed from Dropbox. Continent $0.00 - World $0.00	WonderClub (9297 total ratings)

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Sean Howard

reviewed Neural Networks: An Introduction (Physics of Neural Networks Series) on November 15, 2014

Neural Networks: An Introduction (Physics of Neural Networks Series)

Fascinating read but quite outdated

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Neural Networks: An Introduction (Physics of Neural Networks Series), Neural Networks presents concepts of neural-network models and techniques of parallel distributed processing in a three-step approach: - A brief overview of the neural structure of the brain and the history of neural-network modeling introduces to , Neural Networks: An Introduction (Physics of Neural Networks Series)

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Neural Networks: An Introduction (Physics of Neural Networks Series) Book

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1	The Structure of the Central Nervous System	3
2	Neural Networks Introduced	13
3	Associative Memory	24
4	Stochastic Neurons	38
5	Cybernetic Networks	46
6	Multilayered Perceptrons	52
7	Applications	63
8	More Applications of Neural Networks	72
9	Network Architecture and Generalization	93
10	Associative Memory: Advanced Learning Strategies	108
11	Combinatorial Optimization	126
12	VLSI and Neural Networks	135
13	Symmetrical Networks with Hidden Neurons	144
14	Coupled Neural Networks	151
15	Unsupervised Learning	162
16	Evolutionary Algorithms for Learning	174
17	Statistical Physics and Spin Glasses	191
18	The Hopfield Network for [actual symbol not reproducible]	201
19	The Hopfield Network for Finite p/N	209
20	The Space of Interactions in Neural Networks	231
21	Numerical Demonstrations	249
22	ASSO: Associative Memory	253
23	ASSCOUNT: Associative Memory for Time Sequences	264
24	PERBOOL: Learning Boolean Functions with Back-Prop	268
25	PERFUNC: Learning Continuous Functions with Back-Prop	275
26	Solution of the Traveling-Salesman Problem	279
27	KOHOMAP: The Kohonen Self-organizing Map	291
28	BTT: Back-Propagation Through Time	296
29	Neurogen: Using Genetic Algorithms to Train Networks	303
	References	307
	Index	325