User recommended resources
Estimated time needed to finish:
Extracting Features Using Convolution And Regularization
Total time needed:
Feature extraction via convolution kernels
Potential Use Cases
Building a DL architecture for for computer vision model.
Who is This For ?
Click on each of the following
to see details.
1. Mathematics of Convolution
Why wee need more than MLP?
What is a convolution and kernel?
What is a Hann function and how to apply it?
What are ConvNets and what is their architecture?
2. Why Convolutions? Sobel & Scharr Filters
Why to use Convolutions?
What is an image filter?
What is image segmentation and how is it done?
What convolution does really do?
What are two famous and edge detection filters / algorithms?
3. 2D Convolutions, Pooling, and Dilated Convolutions
What is pooling and padding?
What other 2D convolution techniques do exist?
When do we use padding and how?
When do we use pooling and how?
What are dilated convolutions and how they differ from standard convolutions?
How can I apply Sobel and Scharr Operator on image?
What is the difference between Sobel and Scharr Operator and how can I visually compare them?
What is a Convolutional Neural Network (CNN)?
How can one understand a CNN?
5. Regularization using Dropouts
Why do we need dropouts?
6. Hands-on Convolutional Networks
How to implement convnets in PyTorch?
7. Understanding Convolution