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Deep Learning Prerequisites: Linear Regression in Python
Getting Started With The Outline
Introduction and Outline (7:22)
Define the model in 1-D, derive the solution (Updated Version) (4:33)
Define the model in 1-D, derive the solution (7:29)
Define the model in 1-D, derive the solution (8:24)
Determine how good the model is - r-squared (5:56)
R-squared in code (5:59)
Demonstrating Moore's Law in Code (3:28)
Define the multi-dimensional problem and derive the solution (Updated Version) (7:52)
Define the multi-dimensional problem and derive the solution (1:33)
How to solve multiple linear regression using only matrices (4:45)
Coding the multi-dimensional solution in Python (11:57)
Polynomial regression - extending linear regression (with Python code) (4:28)
Predicting Systolic Blood Pressure from Age and Weight (4:05)
What do all these letters mean? (4:22)
Interpreting the Weights (14:49)
Generalization error, train and test sets (3:42)
L1 Regularization - Theory (1:40)
L2 Regularization - Theory (3:00)
L1 vs L2 Regularization (9:02)
The Dummy Variable Trap (7:51)
Gradient Descent Tutorial (19:09)
Gradient Descent for Linear Regression (23:20)
Bypass the Dummy Variable Trap with Gradient Descent (3:51)
Categorical inputs (5:43)
Probabilistic Interpretation of Squared Error (30:04)
Define the multi-dimensional problem and derive the solution (Updated Version)
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