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