Convolutional Neural Networks

Convolutional Neural Networks

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Intro

1 of 30

1 of 30

Intro

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Classroom Contents

Convolutional Neural Networks

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  1. 1 Intro
  2. 2 Images are Numbers
  3. 3 Tasks in Computer Vision
  4. 4 High Level Feature Detection
  5. 5 Manual Feature Extraction
  6. 6 Learning Feature Representations
  7. 7 Fully Connected Neural Network
  8. 8 Using Spatial Structure
  9. 9 Applying Filters to Extract Features
  10. 10 Feature Extraction with Convolution
  11. 11 Filters to Detect X Features
  12. 12 The Convolution Operation
  13. 13 Producing Feature Maps
  14. 14 Convolutional Layers: Local Connectivity
  15. 15 Introducing Non-Linearity
  16. 16 Pooling
  17. 17 CNNs for Classification: Feature Learning
  18. 18 CNNs for Classification: Class Probabilities
  19. 19 CNNs: Training with Backpropagation
  20. 20 ImageNet Dataset
  21. 21 ImageNet Challenge: Classification Task
  22. 22 An Architecture for Many Applications
  23. 23 Beyond Classification
  24. 24 Semantic Segmentation: FCNs
  25. 25 Driving Scene Segmentation
  26. 26 Image Captioning using RNNS
  27. 27 Impact: Face Detection
  28. 28 Impact: Self-Driving Cars
  29. 29 Impact: Healthcare
  30. 30 Deep Learning for Computer Vision: Summary

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