OpenClassroom
Linear Regression I: Gradient Descent
Deep Learning
Lecture 4 of 23
< Previous
Go to Video:
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
Next >