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From Traditional Vision to Deep Learning
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Classroom Contents
Deep Learning for Computer Vision
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- 1 Course Introduction
- 2 History
- 3 Image Formation
- 4 Image Representation
- 5 Linear Filtering
- 6 Image in Frequency Domain
- 7 Image Sampling
- 8 Edge Detection
- 9 From Edges to Blobs and Corners
- 10 Scale Space, Image Pyramids and Filter Banks
- 11 Feature Detectors : SIFT and Variants
- 12 Image Segmentation
- 13 Other Feature Spaces
- 14 Human Visual System
- 15 Feature Matching
- 16 Hough Transform
- 17 From Points to Images:Bag-of-Words and VLAD Representations
- 18 Image Descriptor Matching
- 19 Pyramid Matching
- 20 From Traditional Vision to Deep Learning
- 21 Neural Networks: A Review - Part 1
- 22 Neural Networks: A Review - Part 2
- 23 Feedforward Neural Networks and Backpropagation - Part 1
- 24 Feedforward Neural Networks and Backpropagation - Part 2
- 25 Gradient Descent and Variants - Part 1
- 26 Gradient Descent and Variants - Part 2
- 27 Regularization in Neural Networks - Part 1
- 28 Regularization in Neural Networks - Part 2
- 29 Improving Training of Neural Networks - Part 1
- 30 Improving Training of Neural Networks - Part 2
- 31 Convolutional Neural Networks: An Introduction - Part 01
- 32 Convolutional Neural Networks: An Introduction - Part 02
- 33 Backpropagation in CNNs
- 34 Evolution of CNN Architectures for Image Classification-Part 01
- 35 Evolution of CNN Architectures for Image Classification-Part 02
- 36 Recent CNN Architectures
- 37 Finetuning in CNNs
- 38 Explaining CNNs: Visualization Methods
- 39 Explaining CNNs: Early Methods
- 40 Explaining CNNs: Class Attribution Map Methods
- 41 Explaining CNNs: Recent Methods - Part 01
- 42 Explaining CNNs: Recent Methods -Part 02
- 43 Going Beyond Explaining CNNs
- 44 CNNs for Object Detection I PART 01
- 45 CNNs for Object Detection I PART 02
- 46 CNNs for Object Detection II
- 47 CNNs for Segmentation
- 48 CNNs for Human Understanding Faces- Part 01
- 49 CNNs for Human Understanding Faces PART 02
- 50 CNNs for Human Understanding Human Pose and Crowd
- 51 CNNs for Other Image Tasks
- 52 Recurrent Neural Networks Introduction
- 53 Backpropagation in RNNs
- 54 LSTMs and GRUs
- 55 Video Understanding using CNNs and RNNs
- 56 Attention in Vision Models: An Introduction
- 57 Vision and Language: Image Captioning
- 58 Beyond Captioning: Visual QA, Visual Dialog
- 59 Other Attention Models
- 60 Self-Attention and Transformers
- 61 Deep Generative Models: An Introduction
- 62 Generative Adversarial Networks-Part 01
- 63 Generative Adversarial Networks-Part 02
- 64 Variational Autoencoders
- 65 Combining VAEs and GANs
- 66 Beyond VAEs and GANs: Other Deep Generative Models-01
- 67 Beyond VAEs and GANs: Other Deep Generative Models-02
- 68 GAN Improvements
- 69 Deep Generative Models across Multiple Domains
- 70 VAEs and DIsentanglement
- 71 Deep Generative Models: Image Applications
- 72 Deep Generative Models: Video Applications
- 73 Few-shot and Zero-shot Learning - Part 01
- 74 Few-shot and Zero-shot Learning - Part 02
- 75 Self-Supervised Learning
- 76 Adversarial Robustness
- 77 Pruning and Model Compression
- 78 Neural Architecture Search
- 79 Course Conclusion
