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 itemsin 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??