3.2 - The Bias-Variance Decomposition (with Code!) - Pattern Recognition and Machine Learning
36:27
3.3.1 Bayesian Linear Regression: Parameter Distribution (with Code!) - PRML
12:25
Bias-Variance Tradeoff : Data Science Basics
20:27
Regularization Part 1: Ridge (L2) Regression
24:59
3.5.3 Effective Number of Parameters - Pattern Recognition and Machine Learning
16:11
3.5.1 Evaluation of the Evidence Function - Pattern Recognition and Machine Learning
12:51
Singular Value Decomposition (SVD): Mathematical Overview
29:29
Denoising Diffusion Probabilistic Models | DDPM Explained
49:10