Linear Algebra 10 minutes a day, complete in a month

Name: Linear Algebra 10 minutes a day, complete in a month
Price: 55000 KRW
Rating: 4.6 (5 reviews)

Master the linear algebra content required for AI/deep learning by investing 10 minutes a day and completing it in one month.

(4.6) 수강평 5개

강의소개.상단개요.수강생.short

난이도 초급

수강기한 무제한

jhim21

Linear Algebra

Procession

Deep Learning(DL)

Linear Algebra

Procession

Deep Learning(DL)

강의상세_배울수있는것_타이틀

Focus on studying only the core contents necessary for AI/deep learning
The parts that require mathematical theory are detailed.
Use specific examples to explain the theory in your head

Multidimensional Data Representation: Various data, such as images, text, and voice, are represented as multidimensional vectors. Linear algebra is essential for handling and transforming this multidimensional data.

Image Processing: In computer vision, images are represented as matrices. Matrix operations are used to transform and filter images, which are primarily used in convolutional neural networks (CNNs).

Linear transformations: Machine learning models, such as neural networks, primarily use linear transformations (matrix multiplication) to transform and process input data. Linear algebra provides tools for effectively handling these transformations.

Matrix Decomposition: Linear algebraic techniques such as singular value decomposition (SVD) and eigenvalue decomposition are used in dimensionality reduction techniques such as principal component analysis (PCA) to help extract important features.

Optimization problems: Optimization problems commonly used in machine learning are based on linear algebraic concepts. For example, parameter updates to minimize a loss function involve matrix differentiation and gradient descent.

Studying the above topics requires a significant investment of time and effort. Even implementing the programming itself, if you want to demonstrate something immediately, requires significant time . If you decide to pursue graduate school, you'll often find yourself struggling with math.

I majored in mathematics and spent many years researching and studying with engineering students at an engineering school. I've thought a lot about how to make mathematics easier to learn, but still learn in a mathematical way.

So, in this lecture , we chose to study the theory by solving specific examples and using them.

And with so much content, you can often feel overwhelmed before even starting. I've created a curriculum that's both accessible and packed with essential content.

Although I am busy, I have tried to consistently invest 10 to 20 minutes a day to keep up.

강의소개.콘텐츠.추천문구

학습 대상은 누구일까요?

Machine learning, deep learning, computer vision, computer graphics, and engineering people
I also recommend it to those who studied liberal arts.
I also recommend it to people who lack basic math skills.

선수 지식, 필요할까요?

The will to do it is essential
People who can invest consistently for a month

강의소개.지공자소개

223

수강생

수강평

답변

4.6

강의 평점

강의_other

After graduating with my PhD, I had the opportunity to study and teach computer vision for about five years, which led me to

Up until now, I have been focusing my studies on bridging the gap between my mathematics major and engineering theories.

Areas of Expertise (Fields of Study)

Major: Mathematics (Topological Geometry), Minor: Computer Science

Current) 3D Computer Vision (3D Reconstruction), Kalman Filter, Lie-group (SO(3)),

Researcher in Stochastic Differential Equations

Current) YouTube Channel Host: Jang-hwan Lim: 3D Computer Vision

Current) Facebook Spatial AI KR Group (Mathematics Advisory Committee Member)

Education

PhD in Natural Sciences, University of Kiel, Germany (Major in Topological Geometry & Lie-group, Minor in Computer Science)

Bachelor's and Master's (Topology major) in Mathematics, Chung-Ang University

Experience

Former) CTO of Doobivision, a subsidiary of Daesung Group

Former Research Professor at Chung-Ang University Graduate School of Advanced Imaging (3D Computer Vision Research)

Books:

Optimization Theory: https://product.kyobobook.co.kr/detail/S000200518524

Link

YouTube: https://www.youtube.com/@3dcomputervision

Blog: https://blog.naver.com/jang_hwan_im

er Vision Research) Author of: Optimization Theory: https://product.kyobobook.co.kr/detail/S000200518524 Link YouTube: https://www.youtube.com/@3dcomputervision Blog: https://blog.naver.com/jang_hwan_im

er Vision Research) Author: Optimization Theory: https://product.kyobobook.co.kr/detail/S000200518524 Link YouTube: https://www.youtube.com/@3dcomputervision Blog: https://blog.naver.com/jang_hwan_im

커리큘럼

전체

31개 ∙ (강의상세_런타임_시간 강의상세_런타임_분)

해당 강의에서 제공: [object Object]

섹션 1. 0.1 Definition of matrix, matrix operations, introduction to linear equations

4개 ∙ (강의상세_런타임_분)

1. 0.1 Introduction to Linear Algebra
11:40
2. 0.1 Matrix Addition
05:40
3. 0.2 Matrix Multiplication
10:17
4. Introduction to Linear Equations
14:58

섹션 2. 1. Introduction to vector space

7개 ∙ (강의상세_런타임_시간 강의상세_런타임_분)

5. The Beginning of Vector Concepts
08:51
6. Subspace: closed Vector space within a Vector Space
12:00
7. Linear combination & linear independence
09:27
8. Basis and Vector Space Dimension
16:32
9. Inner product
17:27
10. Gram-Schmidt Orthogonalization Process
19:38
11. Detailed proof of Theorem 1.30
11:23

섹션 3. 3 perspectives on matrices

8개 ∙ (강의상세_런타임_시간 강의상세_런타임_분)

12. Three Perspectives on Viewing Matrices
14:24
13. Matrix: Function between Vector Spaces
13:39
14. Vector space connection function example
06:54
15. The multiplication of matrices is a composition of linear functions.
03:22
16. Rank of matrix
11:35
17. Determinant
14:58
18. Orthogonal matrix
05:23
19. cross product
03:33

섹션 4. Eigenvalue Eigenvector

6개 ∙ (강의상세_런타임_시간 강의상세_런타임_분)

섹션 5. Mahalla Nobis Street

1개 ∙ (강의상세_런타임_분)

섹션 6. Decomposition of matrices

3개 ∙ (강의상세_런타임_분)

섹션 7. How to solve linear equations

2개 ∙ (강의상세_런타임_분)

강의 게시일: 11/21/2023

마지막 업데이트일: 05/13/2025

수강평

전체

5개

4.6

5개의 수강평

jaewonchang8043
수강평 1
∙
평균 평점 3.0
08/29/2024
3
100% 수강 후 작성
ilwoongson8186
수강평 1
∙
평균 평점 5.0
08/29/2024
5
61% 수강 후 작성
It's easy and fun!
lsk2128607
수강평 1
∙
평균 평점 5.0
08/28/2024
5
32% 수강 후 작성
I took the course in a subject that I really needed, so it was efficient and the lecture content was very good.
dongjunkang8203
수강평 1
∙
평균 평점 5.0
08/23/2024
5
100% 수강 후 작성
taelimsong8179
수강평 1
∙
평균 평점 5.0
08/20/2024
5
32% 수강 후 작성

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