HUAHUA
Machine_learnings
DEEP LEARNING THEORY
Paper Reading
Batch Normalization
Scratch
MACHINE_LEARNING_THEORY
Reference
Linear Regression
§1 prior knowledge
§2 Linear Regression
§3 Logistic Regression
§4 Generalized Linear Models
Generative learning algorithms
What are Generative learning algorithms?
§1 Gaussian discriminant analysis
§2 Naive bayes
Support vector machines
Margin
The optimal margin classifier
Lagrange duality
Neural Network
What is Neural Network?
Activation Function
Backpropagation
Reference
Generalization
Bias-Variance of Model
Double descent phenomenon
Sample complexity bound
Regularization
Regularization
Implicit regularization effect
Bayes statistics
Evaluation
Cross Validation
Performance
K-means alogrithm
EM algorithm
PROBABILITY
§1 Probability Introduction
Element of Probability
Permutation & Combination
Conditional Probability
Law of total probability
Bayes’ theorem
Common distribution
§2 Random Variables
Cumulative distribution functions
Probability mass function
Probability denstiy function
Expectation
Variance
NATURAL_LANGUAGE_GENERATION
Recurrent Neural Network
Milestone papers
Paper Reading