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Swin transformer - Explained!
00:27:55
DETR - Explained!
00:18:03
Vision Transformers - Explained!
00:22:21
Feature Pyramid Networks - Explained!
00:27:31
Depthwise Separable Convolutions - Explained!
00:15:16
Mask R-CNN - Explained!
00:28:46
YOLO - Explained!
00:24:02
Faster R-CNN - Explained!
00:28:50
ResNet - Explained!
00:16:13
Fast R-CNN - Explained!
00:36:56
VGGNet  - Explained!
00:14:04
Inception Net - Explained! (with code)
00:15:46
Pointwise Convolutions - EXPLAINED (with code)
00:22:01
Deconvolution - what do networks learn? (visualization + code)
00:22:44
R-CNN - Explained!
00:18:18
Region Proposals - Explained!
00:15:13
Image segmentation - Explained!
00:15:24
How was object detection done before neural networks?
00:15:06
Why neural networks are so deep? (AlexNet - Explained)
00:22:50
Visualizing convolution networks
00:37:38
Why convolution networks work so well (on images)
00:14:31
Convolution Network back propagation by hand | the math you should know!
00:53:46
Back propagation by hand | the math you should know
00:36:32
Where did convolution networks come from?
00:33:13
Primary Visual Cortex: How brain processes what we see
00:15:33
Receptive Fields - Explained
00:12:50
Visual Pathway - Explained
00:11:39
Vision: Structure of the eye - Explained!
00:08:41
Reinforcement Learning: Zero to Hero
01:38:01
Evolution of Neural Networks: Zero to Hero
02:22:49