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