Chapter 6 優化學習
6-1 Steepest Descent Approach 6-2 The Analysis of Learning Convergence I 6-3 The Analysis of Learning Convergence II 6-4 Newton Approaching Method
6-1 Steepest Descent Approach 6-2 The Analysis of Learning Convergence I 6-3 The Analysis of Learning Convergence II 6-4 Newton Approaching Method
5-1 One Dimensional Taylor Expansion 5-2 High Dimensional Taylor Expansion 5-3 The Concept of Optimization 5-4 Minima and Maxima
4-1 Linear Mapping 4-2 Matrix Representation 4-3 Eigenvalue and Eigenvector 4-4 Quadratic Form 4-5 Principal Axis Theorem 4-6 Matrix Norm and Condition Number
3-1 Vector Space 3-2 Linear independent and depend 3-3 Basis 3-4 Basis Translation 3-5 Row and Column Space 3-6 Rank
2-1 感知元件之基本結構 2-2 感知元件之多層結構 2-3 感知元件之矩陣計算模式 2-4 感知網路之認知功能 2-5 認知學習演算法