Completed
What can Deep Neural Networks do?
Class Central Classrooms beta
YouTube videos curated by Class Central.
Classroom Contents
The Mathematics of Artificial Intelligence
Automatically move to the next video in the Classroom when playback concludes
- 1 Intro
- 2 The Dawn of Artificial Intelligence in Public Life
- 3 Spectacular Success in Science
- 4 Impact on Mathematical Problem Settings
- 5 Artificial Intelligence = Alchemy?
- 6 Problem with Reliability
- 7 Role of Mathematics Two Key Challenges for Mathematics
- 8 First Appearance of Neural Networks
- 9 Artificial Neurons
- 10 Affine Linear Maps and Weights
- 11 Definition of a Deep Neural Network
- 12 Training of Deep Neural Networks
- 13 Mathematics for Artificial Intelligence
- 14 Glimpse into Generalization
- 15 Graph Neural Networks
- 16 A Special Form of Generalization Capability
- 17 Generalization Result
- 18 Glimpse into Explainability
- 19 Artificial Intelligence for Mathematics
- 20 Anisotropic Structures as Model for Images
- 21 (Cone-adapted) Discrete Shearlet Systems
- 22 Optimally Sparse Approximation Theorem (K. Lin, 2011)
- 23 Solving Inverse Problems
- 24 (Limited Angle) Computed Tomography
- 25 Zooming in on the Limited-Angle CT Problem
- 26 Numerical Results
- 27 Deep Network Shearlet Edge Extractor (DeNSE)
- 28 Numerical Deep Learning Approaches to PDES
- 29 What can Deep Neural Networks do?
- 30 Theoretical Results
- 31 A Serious Problem Computability on Digital Machines (informal)
- 32 Some Thoughts on the Result
- 33 Conclusions
