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An Architecture for Many Applications
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Classroom Contents
Convolutional Neural Networks
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- 1 Intro
- 2 Vision: Evolutionary Origins
- 3 The Visual Cortex
- 4 Images are Numbers
- 5 Tasks in Computer Vision
- 6 High Level Feature Detection
- 7 Manual Feature Extraction
- 8 Learning Feature Representations
- 9 Fully Connected Neural Network
- 10 Using Spatial Structure
- 11 Applying Filters to Extract Features
- 12 Filters to Detect X Features
- 13 The Convolution Operation
- 14 Producing Feature Maps
- 15 Feature Extraction with Convolution
- 16 Convolutional Layers: Local Connectivity
- 17 Introducing Non-Linearity
- 18 Pooling
- 19 CNNs for Classification: Feature Learning
- 20 CNNs: Training with Backpropagation
- 21 ImageNet Dataset
- 22 ImageNet Challenge: Classification Task
- 23 An Architecture for Many Applications
- 24 Beyond Classification
- 25 Semantic Segmentation: FCNS
- 26 Driving Scene Segmentation
- 27 Object Detection with R-CNN
- 28 Image Captioning using RNNS
- 29 Class Activation Maps (CAM)
- 30 Data, Data, Data
- 31 Deep Learning for Computer Vision: Impact
- 32 Impact: Face Recognition
- 33 Impact: Self-Driving Cars
- 34 Impact: Medicine nature
- 35 Deep Learning for Computer Vision: Review
