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Intro
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Classroom Contents
Convolutional Neural Networks
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- 1 Intro
- 2 To discover from images what is present in the world, where things are, what actions are taking place, to predict and anticipate events in the world
- 3 The rise and impact of computer vision
- 4 Impact: Self-Driving Cars
- 5 Impact: Medicine, Biology, Healthcare
- 6 Images are Numbers
- 7 Tasks in Computer Vision
- 8 Manual Feature Extraction
- 9 Learning Feature Representations Can we learn a hierarchy of features directly from the data instead of hand engineering
- 10 Fully Connected Neural Network
- 11 Using Spatial Structure
- 12 Feature Extraction with Convolution
- 13 Filters to Detect X Features
- 14 The Convolution Operation
- 15 Producing Feature Maps
- 16 Convolutional Layers: Local Connectivity
- 17 Introducing Non-Linearity
- 18 Pooling
- 19 Putting it all together
- 20 An Architecture for Many Applications
- 21 Classification: Breast Cancer Screening
- 22 Semantic Segmentation: Fully Convolutional Networks
- 23 Continuous Control: Navigation from Vision
- 24 End-to-End Framework for Autonomous Navigation
- 25 Deep Learning for Computer Vision: Summary
