Module 5- Part 3- What CNN architecture? Interpretable CNN and Transfer Learning in computer vision

Relevant playlists:
Machine Learning Concepts, simply explained: Machine Learning Concepts (Simply Exp...
Deep Learning Concepts, simply explained: Deep Learning Concepts (Simply Explai...

Instructor: Pedram Jahangiry

All of the slides and notebooks used in this series are available on my GitHub page, so you can follow along and experiment with the code on your own.
https://github.com/PJalgotrader

Lecture Outline:
0:00 Roadmap and recap
1:28 Convnet architecture
4:05 MHR formula (Modularity, Hierarchy, Reuse)
6:10 Convnet architecture best practices
7:24 Residual connections
9:43 Batch Normalization
12:20 Depthwise separable convolutions
15:30 Best practices summary
18:47 Interpreting convnets (visualizing intermediate convnet output, filters and CAM: class activation map)
29:30 Classical CNN architectures
30:50 LeNet-5
33:28 AlexNet
34:49 VGG16
37:11 ResNet
39:51 Comparing the performance of classical CNN models on ImageNet data
43:00 State of the art models (as of March 2023)
44:00 Transfer learning (feature extraction, fine tuning)