Covers: theory of Convolution Motivation
Estimated time needed to finish: 15 minutes
Questions this item addresses:
- What are the main ideas for Convolutional Neural Networks (CNN)?
- What are sparse interactions?
- What is sparse interactions?
- What is equivariance?
How to use this item?
Read the section 9.2 corresponding to the motivation to Convolutional Neural Networks. The section explains important concepts: sparse interactions, sparse interactions and equivariance
Author(s) / creator(s) / reference(s)
Goodfellow e al.
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Understanding Convolution Motivation
Collaborators
- 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.
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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