Covers: theory of convolutions
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Extracting Features Using Convolution And Regularization

Total time needed: ~4 hours
Feature extraction via convolution kernels
Potential Use Cases
Building a DL architecture for for computer vision model.
Who is This For ?
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VIDEO 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?
31 minutes
VIDEO 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?
16 minutes
VIDEO 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?
30 minutes
VIDEO 4. Conv-Nets
  • 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?
20 minutes
VIDEO 5. Regularization using Dropouts
  • Why do we need dropouts?
17 minutes
REPO 6. Hands-on Convolutional Networks
  • How to implement convnets in PyTorch?
30 minutes
RECIPE 7. Understanding Convolution
40 minutes

Concepts Covered

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