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