Overview
Syllabus
Intro
Ideal Features
The Manifold of Natural Images
Ideal Feature Extraction
Learning Non-Linear Features
Linear Combination prediction of class
A Potential Problem with Deep Learning
Deep Learning in Practice
KEY IDEAS: WHY DEEP LEARNING
Buzz Words
(My) Definition
ConvNets: today
Deep Gated MRF
Sampling High-Resolution Images
Sampling After Training on Face Images
Cons
CONV NETS: TYPICAL ARCHITECTURE
CONV NETS: EXAMPLES
CHOOSING THE ARCHITECTURE
HOW TO OPTIMIZE
HOW TO IMPROVE GENERALIZATION
OTHER THINGS GOOD TO KNOW
WHAT IF IT DOES NOT WORK?
Taught by
Meta Developers