Neural Networks : A Classroom Approach, 2/e (Paperback)

Satish Kumar

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  • 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.

目錄大綱

  • 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