Neural Networks : A Classroom Approach, 2/e (Paperback)
暫譯: 神經網絡：課堂教學法，第二版（平裝本）

Satish Kumar

Neural Networks : A Classroom Approach, 2/e (Paperback)
  • 出版商: McGraw-Hill Education
  • 出版日期: 2013-01-01
  • 定價: $1,150
  • 售價: 9.8 折 $1,127
  • 語言: 英文
  • 頁數: 735
  • ISBN: 1259006166
  • ISBN-13: 9781259006166
  • 相關分類: DeepLearning

    • 已絕版

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商品描述

  • 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.

商品描述(中文翻譯)

這本《神經網絡》的修訂版是該主題的最新闡述，持續提供對基礎神經網絡模型背後幾何結構的理解，同時強調理論結果的啟發式解釋。本書的亮點在於其易讀的格式，以及理論與實踐的平衡混合，並不妥協所需的數學嚴謹性。Kumar 教授在本書中成功地維持了優秀的圖像描述，與概念和有趣的教學法相結合，以提供扎實的學習體驗。

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

目錄大綱(中文翻譯)


  • 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

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