Neural Network Design (2nd Edition)

【实例简介】
Neural Network Design (2nd Edition)为大牛Martin T. Hagan巨作，深入浅出地讲解了深度学习的所有算法原理，非常适合全面深入了解人工神经网络及深度学习
Copyright by Martin T Hagan and Howard B Demuth. All rights reserved. No part of the book may be reproduced stored in a retrieval system, or transcribed in any form or by any means electronic, mechanical, photocopying, recording or otherwise-without the prior permission of Hagan and Demuth MTH To Janet. Thomas Daniel. Mom and dad HBD To Hal, Katherine, Kimberly and Mary MHB To Leah. Valerie. Asia Drake. coi d Morgan ODJ To: Marisela, Maria victoria, Manuel, Mamd y papa Neural Network Design, 2nd Edition, eBook oVERHEADS and DEMONSTRATION PRoGraMs can be found at the following website hagan. okstate. edu/nnd.html A somewhat condensed paperback version of this text can be ordered from Amazon Contents reface Introduction Objectives History 1-2 Applications Biological Inspiration Further Reading Neuron model and Network architectures h Objectives Theory and Examples 2-2 Notation 2-2 Neuron model sing」le- Input Neuron 2-2 Transfer functions 2-3 Multiple-Input Neuron Network Architectures A Layer of Neurons 2-9 Multiple Layers of Neurons 2-10 Recurrent Networks 2-13 Summary of Results 2-16 Solved Problems 2-20 plogue 2-22 Exercises 2-23 An illustrative Example 3 z Objectives Theory and Examples 3-2 Problem statement 3-2 Perceptron 3-3 Two-Input case 3-4 Pattern Recognition example Hamming network 3-8 Feedforward layer 3-8 Recurrent Layer 3-9 Hopfield Network 3-12 Epilogue 3-15 Exercises 3-16 Perceptron Learning rule Objectives Theory and EXamples 4-2 Learning Rules 4-2 Perceptron Architecture 4-3 Single-Neuron Perceptro 4-5 Multiple-Neuron Perceptron 4-8 Perceptron Learning Rule 4-8 Test proble 4-9 Constructing Learning Rules 4-10 Unified Learning Rule 4-12 Training Multiple-Neuron Perceptrons 4-13 Proof of Convergence 4-15 Notation 4-15 Proof 4-16 Limitations 4-18 Summary of Results 4-20 Solved problems 4-21 Epilogue 4-33 urther Reading 4-34 Exercises 4-36 Signal and Weight Vector Spaces 5 Objectives Theory and Examples 5-2 Linear Vector Spaces Linear Independence Spanning a Space 5-5 Inner product Norm 5-7 Orthogonality 5-7 Gram-Schmidt Orthogonalization 5-8 Vector Expansions Reciprocal Basis vectors 5-10 Summary of Results 5-14 Solved Problems 5-17 Epilogue 5-26 Further Reading 5-27 Exercises 5-28 6 Linear transformations for Neural networks Objectives Theory and Examples Linear Transformations 6-2 Matrix Representations Change of Basis 6-6 Eigenvalues and Eigenvectors 6-10 Diagonalization 6-13 Summary of Results 6-15 Solved problems 6-17 Epilogue 6-28 Further Reading 6-29 xercises 6-30 Supervised Hebbian Learning 7 Objectives 7-1 Theory and Examples 7-2 inear associato 7-3 The Hebb rule 7-4 Performance Analysis 7-5 Pseudoinverse rule Application 7-10 Variations of hebbian le 7-12 su ummary of results y 17-4 Solved Problems Epilogue 7-29 Further readi 7-30 Exercises 7-31 8 Performance Surfaces and Optimum Points Objectives Theory and Examples 8-2 Taylor Series 8-2 Vector case 8-4 Directional derivati Minima Necessary Conditions for Optimality First-Order conditions 8-10 Second-Order Conditions 8-11 Quadratic Functions 8-12 Eigensystem of the Hessian 8-13 Summary of results 8-20 Solved problems 8-22 plogue 8-34 Further Reading 8-35 Exercises 8-36 LL Performance Optimization 9 Objectives Theory and Examples 9-2 Steepest Descent 9-2 Stable Learning Rates 9-6 Minimizing along a Line 9-8 Newton's method 9-10 Conjugate Gradient 9-15 Summary of Results 9-21 Solved problems 9-23 Epilogue Further Reading 9-38 Exercises 9-39 10 Widrow-Hoff Learning Objectives 10-1 Theory and EXamples 102 ADALINE Network 10-2 Single ADALINE 10-3 Mean Square Error 10-4 LMS Algorithm 10-7 Analysis of Convergence 10-9 Adaptive Filtering 10-13 Adaptive Noise Cancellation 10-15 Echo Cancellation 10-21 Summary of Results 10-22 Solved Problems 10-24 Epilogue 10-40 Further Reading 10-41 Exercises 10-42 Backpropagation 11 Objectives 11-1 Theory and Examples Multilayer Perceptrons 11-2 Pattern Classification 11-3 Function Approximation 11-4 The Backpropagation algorithm 11-7 Performance Index 11-8 Chain Rule Backpropagating the Sensitivities 11-11 ary 11-13 Example 11-14 Batch vS Incremental Training 11-17 Using Backpropagation 11-18 Choice of Network architecture 11-18 Convergence 11-20 Generalization 1122 Summary of Results 11-25 Solved Problems 11-27 Epilogue 11-41 Further Reading 11-42 Exercises 11-44 12 Variations on Backpropagation Objectives 12-1 Theory and Examples 122 Drawbacks of Backpropagation 12-3 erformance Surface EXample 12-3 Convergence Example 12-7 Heuristic Modifications of Backpropagation 129 Momentum 12-9 Variable Learning Rate 12-12 Numerical Optimization Techniques 12-14 Conjugate gradient 12-14 Levenberg-Marquardt algorithm 12-19 Summary of Results 12-28 Solved Problems 12-32 Epilogue 12-46 Further Reading 12-47 Exercises 12-50 Generalization 13 Objectives 13-1 Theory and Examples 13-2 Problem statement 13-2 Methods for Improving Generalization 13-5 Estimating generalization error 13-6 Early Stopping 13-6 Regularizati 13-8 Bayesian Analysis 13-10 Bayesian Regularization 13-12 Relationship Between Early Stopping and Regularizat 13-19 Summary of Results 13-29 Solved Problems 13-32 13-44 Further Reading 13-45 Exercises 13-47 14 Dynamic Networks Objectives 14-1 Theory and examples 14-2 Layered Digital Dynamic Networks 14-3 Example dynamic Networks 14-5 Principles of Dynamic Learning 14-8 Dynamic Backpropagation 14-12 Preliminary definitions 14-12 Real Time Recurrent Learning 14-12 Backpropagation-Through-Time 14-22 Summary and comments on Dynamic T raining 14-30 Summary of Results 14-34 Solved Problems 14-37 Epilogue 14-46 Further Reading 14-47 Exercises 14-48 【实例截图】
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