Neural Networks: A Classroom Approach 2/e (絕)
- 20本以上，享 8.5折
售價
$
洽詢
- 一般書籍
- ISBN：9781259006166
- 作者：Satish Kumar
- 版次：2
- 年份：2013
- 出版商：McGraw-Hill
- 頁數/規格：735頁/平裝單色
書籍介紹
本書特色
目錄
Description
This revised edition of Neural Networks is an up-to-date exposition of the subject and continues to provide an understanding of the underlying geometry of foundation neural network models while stressing on heuristic explanations of theoretical results. The highlight of this book is its easy-to-read format and a balanced mix of both theory and practice, without compromising on the requisite mathematical rigor. Professor Kumar, in this book, has successfully maintained excellent pictorial description integrated with the concepts and interesting pedagogy to render sound learning.
This revised edition of Neural Networks is an up-to-date exposition of the subject and continues to provide an understanding of the underlying geometry of foundation neural network models while stressing on heuristic explanations of theoretical results. The highlight of this book is its easy-to-read format and a balanced mix of both theory and practice, without compromising on the requisite mathematical rigor. Professor Kumar, in this book, has successfully maintained excellent pictorial description integrated with the concepts and interesting pedagogy to render sound learning.
Features
- Well organized contents - unique in style, sequence, and coverage - to enable conceptual flow
- Includes real-world applications for all foundation models
- Works through each neural network algorithm using solved examples
- New dedicated chapters on ‘The Brain Metaphor’ and ‘Evolutionary Algorithms’
- Integrates Pseudo-code operational summaries and well-documented MATLAB code segments for all models
- Expanded and updated coverage to Neuroscience, Neural networks and Statistical pattern recognition, Support vector machines, Dynamical systems, Fuzzy sets and systems, Soft computing, and Pulsed and Quantum neural networks
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Pedagogy :
- Conversational notes relating to within chapter textual content
- Chapter-end summaries
- Bibliographic remarks
- Chapter-end exercises
Table of Contents
Part I: Traces of History and a Neuroscience Briefer
Chapter 1: The Brain Metaphor
Chapter 2: Lessons from Neuroscience
Part II: Feedforward Neural Networks and Supervised Learning
Chapter 3: Artificial Neurons, Neural Networks and Architectures
Chapter 4: Geometry of Binary Threshold Neurons and Their Networks
Chapter 5: Supervised Learning I: Perceptrons and LMS
Chapter 6: Supervised Learning II: Backpropagation and Beyond
Chapter 7: Neural Networks: A Statistical Pattern Recognition Perspective
Chapter 8: Statistical Learning Theory, Support Vector Machines and Radial Basis Function Networks
Part III: Recurrent Neurodynamical Systems and Unsupervised Learning
Chapter 9: Dynamical Systems Review
Chapter 10: Attractor Neural Networks
Chapter 11: Adaptive Resonance Theory
Chapter 12: Towards the Self-organizing Feature Map
Part IV: Contemporary Topics
Chapter 13: Fuzzy Sets and Fuzzy Systems
Chapter 14: Evolutionary Algorithms
Chapter 15: Soft Computing Goes Hybrid
Chapter 16: Frontiers of Research: Spiking and Quantum Neural Networks
Part I: Traces of History and a Neuroscience Briefer
Chapter 1: The Brain Metaphor
Chapter 2: Lessons from Neuroscience
Part II: Feedforward Neural Networks and Supervised Learning
Chapter 3: Artificial Neurons, Neural Networks and Architectures
Chapter 4: Geometry of Binary Threshold Neurons and Their Networks
Chapter 5: Supervised Learning I: Perceptrons and LMS
Chapter 6: Supervised Learning II: Backpropagation and Beyond
Chapter 7: Neural Networks: A Statistical Pattern Recognition Perspective
Chapter 8: Statistical Learning Theory, Support Vector Machines and Radial Basis Function Networks
Part III: Recurrent Neurodynamical Systems and Unsupervised Learning
Chapter 9: Dynamical Systems Review
Chapter 10: Attractor Neural Networks
Chapter 11: Adaptive Resonance Theory
Chapter 12: Towards the Self-organizing Feature Map
Part IV: Contemporary Topics
Chapter 13: Fuzzy Sets and Fuzzy Systems
Chapter 14: Evolutionary Algorithms
Chapter 15: Soft Computing Goes Hybrid
Chapter 16: Frontiers of Research: Spiking and Quantum Neural Networks
