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Classroom Contents
Gunnar Carlsson: Topological Deep Learning
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- 1 Intro
- 2 What is Deep Learning?
- 3 Problems
- 4 Convolutional Neural Networks
- 5 Mumford Data Set (De Silva, Ishkhanov, Zomorodian, C.)
- 6 Image Patch Analysis: Primary Circle
- 7 Image Patch Analysis: Three Circle Model
- 8 Image Patch Analysis: Klein Bottle
- 9 Primary Visual Cortex
- 10 Visual Pathway
- 11 The Shape of Data
- 12 Topology
- 13 How to Build Networks - Mapper Construction
- 14 Topological Modeling
- 15 Topological Analysis of Weight Spaces (MNIST)
- 16 Topological Analysis of Weight Spaces (Cifar10)
- 17 Topological Analysis of Weight Spaces (VGG16)
- 18 Hard Code Primary Circle and Klein Bottle
- 19 Convolutional Situation
- 20 Discovered Geometry
- 21 Feature Space Modeling
- 22 Microarray Analysis of Breast Cancer
- 23 Explaining the Different Cohorts
- 24 UCSD Microbiome
- 25 Generalized Convolutional Nets
- 26 Metric and Graph Correspondences
- 27 The Mapper Architectures
- 28 Klein Bottle Connections
- 29 Generalization
- 30 Learning on Video
