Covers: theory of Convolution Motivation

- 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?

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

URL: https://www.coursera.org/lecture/intro-to-deep-learning/motivation-for-convolutional-layers-YZnOW

National Research University Higher School of Economics

Sandra Lopez-Zamora**Total time needed: **~38 minutes

- Learning 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
- Target Audience
- BEGINNERDeep Learning practitioners interested in Mathematical foundations

Go through the following **annotated items** *in order*:

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

Previewing stream ** Math and Foundations**

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