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Logistic Regression: Model
Deep Learning
Lecture 12 of 23
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Supervised Learning Introduction
Model Representation
Cost Function
Gradient Descent
Gradient Descent for Linear Regression
Vectorized Implementation
Feature Scaling
Learning Rate
Features and Polynomial Regression
Normal Equations
Classification
Model
Optimization Objective I
Optimization Objective II
Gradient Descent
Newton's Method I
Newton's Method II
Gradient Descent vs Newton's Method
The Problem Of Overfitting
Optimization Objective
Common Variations
Regularized Linear Regression
Regularized Logistic Regression
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