Covers: theory of Convolution Motivation
Estimated time needed to finish: 8 minutes
Questions this item addresses:
  • How can we normalize an input image?
  • What is the difference of MLP and Convolution?
  • What is convolution?
  • What does it mean edge detection, Sharpening and blurring??
  • How convolution and correlation can be similar?
How to use this item?

Watch the first 8 min of the video video to learn why convolution is important. The resource includes examples related with computer vision.

Author(s) / creator(s) / reference(s)
National Research University Higher School of Economics
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Understanding Convolution Motivation

Contributors
Total time needed: ~38 minutes
Objectives
The user of this short list will be able to understand the concepts related to convolution motivation, sparse interactions, parameter sharing and equivariant representations
Potential Use Cases
You are looking to understand the mathematical foundations for Deep Learning
Who is This For ?
BEGINNERDeep Learning practitioners interested in Mathematical foundations
Click on each of the following annotated items to see details.
Resources4/4
VIDEO 1. Computer vision - parameter sharing
  • What's the idea behind parameter sharing?
11 minutes
VIDEO 2. Learning SO(3) Equivariant Representations with Spherical CNNs
  • What is equivariance?
  • Why equivariance is important?
  • What is the effect of using equivariant representations on models?
4 minutes
BOOK_CHAPTER 3. Motivation
  • What are the main ideas for Convolutional Neural Networks (CNN)?
  • What are sparse interactions?
  • What is sparse interactions?
  • What is equivariance?
15 minutes
VIDEO 4. Motivation for convolutional layers
  • How can we normalize an input image?
  • What is the difference of MLP and Convolution?
  • What is convolution?
  • What does it mean edge detection, Sharpening and blurring??
  • How convolution and correlation can be similar?
8 minutes

Concepts Covered

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