Joeyonng
Notebook
Pages
About
Backyard
Machine Learning
41
Multi-layer Perceptron
Welcome
Notations and Facts
Linear Algebra
1
Fields and Spaces
2
Vectors and Matrices
3
Span and Linear Independence
4
Basis and Dimension
5
Linear Map and Rank
6
Inner Product and Norm
7
Orthogonality and Unitary Matrix
8
Complementary Subspaces and Projection
9
Orthogonal Complement and Decomposition
10
SVD and Pseudoinverse
11
Orthogonal and Affine Projection
12
Determinants and Eigensystems
13
Similarity and Diagonalization
14
Normal and Hermitian Matrices
15
Positive Definite Matrices
Calculus
16
Derivatives
17
Chain rule
Probability and Statistics
18
Probability
19
Random Variables
20
Expectation
21
Common Distributions
22
Gaussian Distribution
23
Moment Generating Function
24
Concentration Inequalities I
25
Convergence
26
Limit Theorems
27
Maximum Likelihood Estimation
28
Bayesian Estimation
29
Expectation-maximization
30
Concentration Inequalities II
Learning Theory
31
Statistical Learning
32
Bayesian Classifier
33
Effective Class Size
34
Empirical Risk Minimization
35
Uniform Convergence
36
PAC Learning
37
Rademacher Complexity
Machine Learning
38
Linear Discriminant
39
Perceptron
40
Logistic Regression
41
Multi-layer Perceptron
42
Boosting
43
Support Vector Machine
44
Decision Tree
45
Principle Component Analysis
Table of contents
Preliminary
Calculus
Supervised Learning
Backpropagation
Machine Learning
41
Multi-layer Perceptron
41
Multi-layer Perceptron
Preliminary
Calculus
Chain Rule
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Supervised Learning
Perceptron
Chapter 39
Logistic Regression
Chapter 40
Backpropagation
40
Logistic Regression
42
Boosting