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Shin Kyung-sik's Deep Learning Odyssey - Convolution Operations

This lecture focuses on convolution, the most important operation in computer vision using deep learning.

1 learners are taking this course

Level Beginner

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Computer Vision(CV)
Computer Vision(CV)
Deep Learning(DL)
Deep Learning(DL)
AI
AI
Computer Vision(CV)
Computer Vision(CV)
Deep Learning(DL)
Deep Learning(DL)
AI
AI

What you will gain after the course

  • A solid understanding of convolution

  • Filtering via Convolution

  • Convolution implementation capability

Shin Kyung-sik's Deep Learning Odyssey

Curriculum Map Link

Convolution, the most important operation in computer vision!
A deep understanding of convolution itself

This course focuses intensively on convolution, the most important element of computer vision using deep learning.

This course covers the following content to not only provide a solid theoretical background on convolution, but also to cultivate implementation skills by directly implementing them.

Chapter 1: From Pixel-wise Operations to Neighborhood-based Operations

1.1 Image data with spatial structure

1.2 Local Patch

1.3 Neighborhood-based operations

Chapter 2 Windowing

2.0 Setting Up the Development Environment

2.1 One-dimensional Windowing

2.2 Two-dimensional Windowing

Chapter 3 One-Dimensional Convolution

3.0 Review: Dot Product and Filter Response

3.1 What is 1D Edge Detection?

3.2 One-dimensional edge detection process

3.3 Implementation of 1D Edge Detection

3.4 One-dimensional Correlation and Convolution

Chapter 4 Two-Dimensional Convolution

4.1 Process of Two-Dimensional Convolution

4.2 Edge Detection

4.3 Sobel Filtering Theory

4.4 Implementation of Sobel Filtering

Chapter 5 Image Processing Techniques Using Convolution

5.1 Moving Average Filter

5.2 Edge Detection Using Gradient Magnitude

5.3 Gaussian Blurring

5.4 Image Sharpening

Essential Characteristics and Informativeness of Images

Identify the characteristics of images with spatial structures, and understand the natural flow of 1️⃣ local patch, 2️⃣ local connectivity, and 3️⃣ neighborhood-based operation that emerge to identify these characteristics.

Windowing, the extraction process of local patches!

As a step prior to understanding convolution, you will theoretically understand the windowing process for extracting local patches and learn how to implement it yourself. Then, you will refactor the code into a form optimized for GPU computation.

A solid understanding of 1D convolution!

Before dealing with convolution on images, we will gain a solid understanding of convolution on one-dimensional data and the filtering process. Through this, you can understand the process of extracting local patches that have similar characteristics to the filter.


The leading example of convolution! Sobel filtering


Understand image convolution through Sobel filtering. This not only helps greatly in understanding the convolutional layers of deep learning, but also allows you to learn the basic concepts of image processing.

Introduction to representative technologies utilizing convolution

We introduce the representative engineering technologies that utilize one-dimensional and two-dimensional convolution covered in this lecture.




Recommended for
these people

Who is this course right for?

  • Beginners who want to build a solid foundation in deep learning and computer vision

  • For those who are confused by convolution operations

  • Those who want to understand the implementation process of deep learning operations

Need to know before starting?

  • Shin Kyung-shik's Deep Learning Odyssey - Image Basics for Computer Vision

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19 lectures ∙ (3hr 8min)

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