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
- 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