線形代数学概論
tkn
この講座では線形代数学概論を取り上げ、講義を通じて線形代数学概論をマスターすることができます。
초급
Linear Algebra
Linear Algebra with Python - Using NumPy and SciPy
In this course, you will learn how to solve various matrix calculation related problems using Python's SciPy library. Even if you do not know Python or have only a little prior knowledge, you will be able to solve the given problems.
Reviews from Early Learners
What you will gain after the course
How to solve linear algebra problems using python
Utilizing NumPy and SciPy Libraries
Practical applications of matrix operations and improved computational efficiency
No, is this my story?
🌿 CASE 1 🌿
I finished the Introduction to Linear Algebra course. I've finally mastered it. Throw me any problem and I can solve it flawlessly. But... I need to do the singular value decomposition of a 100 x 100 matrix right now. Seriously... I've mastered linear algebra, but this problem is going to take forever.🌿 CASE 2 🌿
Professor: Approximate this data with a quadratic function by tomorrow.
Student: Okay, I understand. How many data points are there?
Professor: 40,000.
student: ??
Professor: Oh, while we're at it, how about fitting a+b*sinh(x)+c*Log(x) as well? Can we do it quickly?
student: ?????????
🌿 CASE 3 🌿
Now that I'm not in college, I can't use Matlab... It's too expensive... Ha... But how do I solve matrix equations...? I'll have to solve some more in the future, but is there any way to do it?
Let's solve various matrix calculation problems using NumPy and SciPy.
To quickly solve matrix calculation problems, you need to use the NumPy and SciPy libraries in Python. Have you ever wanted to solve matrix equations using a computer? Want to find eigenvalues? Or do you need these functions right away?
In this course, you'll use Python's SciPy library to solve a variety of matrix calculation problems. Even if you don't know Python or have a basic understanding of introductory linear algebra, the content is designed to help you apply the knowledge immediately after taking the course. Don't worry, join us!
Learning Objectives 📜
In this course, you will learn how to solve linear algebra problems on a computer using SciPy and NumPy.
People like this will love hearing this! ✨
- Engineering students and graduate students
- Anyone who needs to find the solution to a matrix equation right away
- For those who want to find eigenvalues and eigenvectors right away
- Anyone who wants to try SVD or needs a solution for the least squares method
- Those who studied Introduction to Linear Algebra
- Anyone interested in learning the SciPy library for Python
Check before taking the class! ✒️
- You can take the course even if you do not have any specialized knowledge of Python.
- Lectures and practical training are conducted simultaneously.
- For those who plan to use the Lapack library in the future, I will explain what Lapack functions are related to the functions used in the course.
- In this lecture, we will not learn numerical analysis theory related to matrix calculations.
Questions to ask before taking the class!
Q. Is it true that matrix equations can be solved on a computer?
A. Of course. It would be stranger if it wasn't solved by computer!
Q. I need to find the eigenvalues of a large matrix right now. How do I do it? I can't solve it manually 😭😭
A. Don't worry. You can get it with just a few lines of Python code. This course is for you.
Q. I have no programming experience... but I want to solve linear algebra problems on a computer!
A. Welcome! It's easy to follow.
Q. I heard Python is slow... Isn't it impractical to learn it?
A. The SciPy functions we'll learn utilize functions written in Fortran (developed over decades!). They offer speed and accuracy that are sufficient for practical use.
Q. Where did you hear that... people usually write the code themselves...?
A. There are linear algebra-related functions that have been developed over decades, not just years. Just knowing how to use them can be a huge help in your life.
Would you like to take this course too?
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Introduction to Linear Algebra
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Integration Basics
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Recommended for
these people
Who is this course right for?
For those who want to learn linear algebra concepts through actual coding practice
Anyone who wants to learn linear algebra using Python libraries NumPy and SciPy
Anyone who wants to expand their knowledge of computer science and mathematics by covering practical linear algebra concepts.
6,272
Learners
174
Reviews
387
Answers
4.7
Rating
7
Courses
새로운것을 배우고 가르치는걸 좋아합니다.
인프런을 통해 많은 분들에게 도움이 되면 좋겠습니다.
전문분야 (+좋아하는 분야) 👨🎓
- 전공: 원자력
- 수학: 선형대수학개론, 대학미적분, 벡터미적분학, 응용미분방정식, 응용해석방정식, 확률과 통계, 수치해석
- 컴퓨터 언어: 포트란(MPI, OpenMP 포함), Javascript (nodeJS), C#, C++, Python, Solidity, …
출신학교
- 박사: 카이스트, 원자력 및 양자공학과, 2011 ~ 2016
- 석사: 카이스트, 원자력 및 양자공학과, 2009 ~ 2011
- 학부: 카이스트, 원자력 및 양자공학과, 2005 ~ 2009
- 고등학교: 경기과학고, 2003 ~ 2005
경력
- 2019 ~: 인프런강사
- 2017 ~ 2018: 스탠다드에너지, 연구소장
- 2016 ~ 2017: 스탠다드에너지, 특수연구 총괄
링크
- 블로그: https://blog.naver.com/bumhee34
- 유튜브: https://www.youtube.com/channel/UCsaTZaR2itIeT0eVHPi1_cw
- 이메일 bumhee34@gmail.com
Curriculum
All
29 lectures ∙ (13hr 31min)
Published:
Last updated:
Reviews
All
8 reviews
5.0
8 reviews
gwlReviews 2
∙
Average Rating 5.0
5
76% enrolled完了授業 25/29、受講時間 10h33m で最初の後期残します。こんなにうまくいかない人なので今残さなければ先延ばしになりそうです。 受講動機：ML勉強中にNumpy slicingを正確に学ぶ必要があり、講座を巡るよりここでカバーになりそうで受講しました。 後期：必要だったNumpy slicingを正確に学ぶ必要がありました。チョ・ボムヒ様の線形代数学講義を選手講はしませんでしたが、従うのに問題ありませんでした。このコースをじっくりと追いつくと、Scipyのlinalg関数の使い方を十分に学びます。関数の使い方の説明と例の構成が入念になり、受講生が '当然分かるだろう'と進むことなく詳しく解いて説明してくれます。そのため、先に上げたチョ・ボムヒ様の線形代数学講義が聞きたくなります。 （インフラをリニューアルしたら割引券解放してもらったんだけど…ありませんね。ㅎㅎ）
- tknInstructor
大切なレビューありがとうございます!!ㅎㅎ 教科書や与えられたカリキュラムがなくて作った講座なので、それなりの努力をして作った講座だから、個人的に愛着がたくさん行く講座です。 講座を見ながら不足したり、更新されてほしい部分があれば、いつでも私にメッセージやメールでフィードバックしていただければ、できるだけ反映させていただきます。 私が思いもよらない部分があるかもしれないので、受講生の皆さんの積極的な意見があれば、講座がさらに更新(!)され、修正されることがあります。 （今はまた別の数学関連講座を作っています。これからも多くの関心をお願いしますよㅎㅎ）
- plan20091286
Reviews 5
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Average Rating 4.6
- cksgh91034063
Reviews 4
∙
Average Rating 4.8
- epicshark70492
Reviews 6
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Average Rating 4.8
$42.90
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