8:41
Vision: Structure of the eye - Explained!
11:39
Visual Pathway - Explained
12:50
Receptive Fields - Explained
15:34
Primary Visual Cortex: How brain processes what we see
33:13
Where did convolution networks come from?
53:46
Convolution Network back propagation by hand | the math you should know!
14:31
Why convolution networks work so well (on images)
37:38
Visualizing convolution networks
22:50
Why neural networks are so deep? (AlexNet - Explained)
15:06
How was object detection done before neural networks?
15:24
Image segmentation - Explained!
15:13
Region Proposals - Explained!
18:18
R-CNN - Explained!
22:44
Deconvolution - what do networks learn? (visualization + code)
22:01
Pointwise Convolutions - EXPLAINED (with code)
15:46
Inception Net - Explained! (with code)
14:04
VGGNet - Explained!
36:56
Fast R-CNN - Explained!
16:13
ResNet - Explained!
28:50
Faster R-CNN - Explained!
24:02
YOLO - Explained!
28:46
Mask R-CNN - Explained!
15:16
Depthwise Separable Convolutions - Explained!
27:31
Feature Pyramid Networks - Explained!
22:21
Vision Transformers - Explained!
18:03
DETR - Explained!
27:55
Swin transformer - Explained!
13:27
CLIP - Explained!
24:06
DINO - Explained!
27:50
Evolution of Autoencoders - Explained!
32:18
DALL-E: Text-to-Image generation - Explained!
7:50
Diffusion Models - Explained!
47:27
Full Timeline of Computer Vision & Neural Networks
16:04
Did Transformers Kill YOLO?
9:05
How Transformers Took Over Computer Vision!
18:24
10 Papers that changed Computer Vision
11:52
What are Foundation Models?
LIVE
Why Do Transformers Look Like This?
14:06
This Model Fixed Object Detection's Biggest Weakness!
15:30
This Model fixed Object Detection’s Biggest Training Problem!
10:10
How did Language change Vision Transformers?
8:02
5 Papers that changed Object Detection FOREVER!
6:01
How Do CNNs See Objects at EVERY Scale?
7:35
What are Image Embeddings?
