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Computer Vision Fundamentals Course – Image Processing, Filtering & Edge Detection
Generative Adversarial Networks-Part 01

Generative Adversarial Networks-Part 01

Computer Vision Fundamentals Course – Image Processing, Filtering & Edge Detection

محتوى الكورس

جميع الدروس
129 درس
34:44 Course Introduction and Overview
25:09 History
22:16 Image Formation
21:10 Image Representation
45:54 Linear Filtering
35:41 Image in Frequency Domain
32:47 Image Sampling
51:39 Edge Detection
39:01 From Edges to Blobs and Corners
29:14 Scale Space, Image Pyramids and Filter Banks
40:31 Feature Detectors : SIFT and Variants
40:15 Image Segmentation
26:03 Other Feature Spaces
27:55 Human Visual System
45:13 Feature Matching
27:32 Hough Transform
26:28 From Points to Images:Bag-of-Words and VLAD Representations
25:05 Image Descriptor Matching
43:45 Pyramid Matching
10:37 From Traditional Vision to Deep Learning
30:28 Neural Networks: A Review - Part 1
19:49 Neural Networks: A Review - Part 2
24:49 Feedforward Neural Networks and Backpropagation - Part 1
22:16 Feedforward Neural Networks and Backpropagation - Part 2
41:02 Gradient Descent and Variants - Part 1
33:22 Gradient Descent and Variants - Part 2
38:54 Regularization in Neural Networks - Part 1
39:43 Regularization in Neural Networks - Part 2
28:48 Improving Training of Neural Networks - Part 1
22:37 Improving Training of Neural Networks - Part 2
26:57 Convolutional Neural Networks: An Introduction - Part 01
35:29 Convolutional Neural Networks: An Introduction - Part 02
20:43 Backpropagation in CNNs
35:18 Evolution of CNN Architectures for Image Classification-Part 01
27:02 Evolution of CNN Architectures for Image Classification-Part 02
38:59 Recent CNN Architectures
16:56 Finetuning in CNNs
25:57 Explaining CNNs: Visualization Methods
20:58 Explaining CNNs: Early Methods
31:17 Explaining CNNs: Class Attribution Map Methods
23:53 Explaining CNNs: Recent Methods - Part 01
26:24 Explaining CNNs: Recent Methods -Part 02
16:00 Going Beyond Explaining CNNs
36:28 CNNs for Object Detection I PART 01
26:53 CNNs for Object Detection I PART 02
35:56 CNNs for Object Detection II
37:30 CNNs for Segmentation
19:48 CNNs for Human Understanding Faces- Part 01
37:54 CNNs for Human Understanding Faces PART 02
27:45 CNNs for Human Understanding Human Pose and Crowd
14:46 CNNs for Other Image Tasks
37:54 Recurrent Neural Networks Introduction
17:38 Backpropagation in RNNs
29:15 LSTMs and GRUs
21:50 Video Understanding using CNNs and RNNs
34:07 Attention in Vision Models: An Introduction
44:37 Vision and Language: Image Captioning
44:12 Beyond Captioning: Visual QA, Visual Dialog
41:18 Other Attention Models
34:35 Self-Attention and Transformers
23:39 Deep Generative Models: An Introduction
23:42 Generative Adversarial Networks-Part 01
27:29 Generative Adversarial Networks-Part 02
34:53 Variational Autoencoders
29:57 Combining VAEs and GANs
25:33 Beyond VAEs and GANs: Other Deep Generative Models-01
22:37 Beyond VAEs and GANs: Other Deep Generative Models-02
36:04 GAN Improvements
37:59 Deep Generative Models across Multiple Domains
30:04 VAEs and DIsentanglement
18:11 Deep Generative Models: Image Applications
30:23 Deep Generative Models: Video Applications
21:31 Few-shot and Zero-shot Learning - Part 01
35:50 Few-shot and Zero-shot Learning - Part 02
28:43 Self-Supervised Learning
44:11 Adversarial Robustness
22:55 Pruning and Model Compression
21:52 Neural Architecture Search
17:58 Course Conclusion
30:27 Neural Networks: A Review - Part 1
19:49 Neural Networks: A Review - Part 2
24:47 Feedforward Neural Networks and Backpropagation - Part 1
22:15 Feedforward Neural Networks and Backpropagation - Part 2
41:00 Gradient Descent and Variants - Part 1
33:22 Gradient Descent and Variants - Part 2
38:43 Regularization in Neural Networks - Part 1
39:43 Regularization in Neural Networks - Part 2
28:47 Improving Training of Neural Networks - Part 1
22:36 Improving Training of Neural Networks - Part 2
26:54 Convolutional Neural Networks: An Introduction - Part 01
35:28 02 Convolutional Neural Networks An Introduction Part 02
20:43 03 Backpropagation in CNNs
35:16 04 Evolution of CNN Architectures for Image Classification Part 01
26:57 Evolution of CNN Architectures: InceptionNet, ResNet
50:18 Newer and Recent CNN Architectures
16:54 Finetuning CNNs
25:34 Visualizing CNNs
36:28 CNNs for Object Detection: Pre-Deep Learning Era and Initial Steps
33:29 CNNs for Object Detection: Two-Stage Models
44:26 CNNs for Object Detection: Single-stage Models
41:56 CNNs for Segmentation
37:38 Recurrent Neural Networks: Introduction
17:38 Backpropagation in RNNs
29:11 LSTMs and GRUs
21:50 Video Understanding using CNNs and RNNs
34:07 Attention in Vision Models: An Introduction
44:35 Soft and Hard Attention: Image Captioning
44:10 Additional Content: Beyond Captioning: Visual QA and Dialog
29:27 Self-Attention and Transformers
29:13 From Transformers to Vision Transformers
39:03 Transformers for Segmentation
46:10 Transformers for Detection
23:39 Deep Generative Models: An Introduction
23:34 Generative Adversarial Networks - Part 1
27:29 Generative Adversarial Networks - Part 2
35:52 GAN Hacks and Improvements
34:53 Variational Autoencoders
29:57 VAEs and Disentanglement
48:20 Introduction to Diffusion Models and DDPMs - Part 1
17:22 Introduction to Diffusion Models and DDPMs - Part 2
15:28 Classifier and Classifier-Free Diffusion Guidance
19:15 Contrastive Learning and History in Face Understanding - Part 1
37:42 Contrastive Learning and History in Face Understanding - Part 2
28:37 Self-Supervised Learning: SimCLR
15:08 Vision Language Models: Introduction and History
25:56 CLIP: The Anchoring Inflection Point
34:33 Beyond CLIP: BLIP, BLIP-2 and CoCA
30:38 From VLMs to Multimodal LLMs
15:49 Course Conclusion