The Geometry of Linear Equations
Gilbert Strang · 39:49
Transposes, Permutations & Vector Spaces R^n
Gilbert Strang · 47:41
Convex Functions — First/Second-Order Conditions & Epigraphs
Stephen Boyd · 1:17:14
Saddle-Point Interpretation, KKT Conditions & Perturbation
Stephen Boyd · 1:09:19
The Learning Problem
Yaser Abu-Mostafa · 1:21:27
Is Learning Feasible?
Yaser Abu-Mostafa · 1:16:48
Building makemore Part 3: Activations, Gradients, Kaiming Init & BatchNorm
Andrej Karpathy · 1:55:57
Is Learning Feasible?
Professor of Electrical Engineering and Computer Science
This lecture explores the fundamental question of generalization in machine learning: how to infer properties of an entire space from a limited sample. It introduces the relationship between in-sample and out-of-sample error, laying the groundwork for statistical learning theory.
Is Learning Feasible? - Can we generalize from a limited sample to the entire space? Relationship between in-sample and out-of-sample. Lecture 2 of 18 of Caltech's Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App - and on the course website -
The Learning Problem
Next · Step 7Building makemore Part 3: Activations, Gradients, Kaiming Init & BatchNorm