MIT 6.S191 - Deep Learning Limitations and New Frontiers
Offered By: Alexander Amini via YouTube
Course Description
Overview
Syllabus
Intro
T-shirts! Today!
Course Schedule
Final Class Project
The Rise of Deep Learning
Power of Neural Nets
Artificial Intelligence "Hype": Historical Perspective
Rethinking Generalization
Capacity of Deep Neural Networks
Neural Networks as Function Approximators Neural networks are excellent function approximators
Adversarial Attacks on Neural Networks
Synthesizing Robust Adversarial Examples
Neural Network Limitations...
Why Care About Uncertainty?
Bayesian Deep Learning for Uncertainty
Elementwise Dropout for Uncertainty
Model Uncertainty Application
Multi-Task Learning Using Uncertainty
Motivation: Learning to Learn
AutoML: Learning to Learn
AutoML: Model Controller At each stes, the model samples a brand new network
AutoML:The Child Network
AutoML on the Cloud
AutoML Spawns a Powerful Idea
Taught by
https://www.youtube.com/@AAmini/videos
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