In this course on Linear Algebra we look at what linear algebra is and how it relates to vectors and matrices. Then we look through what vectors and matrices are and how to work with them, including the knotty problem of eigenvalues and eigenvectors, and how to use these to solve problems. Finally we look at how to use these to do fun things with datasets - like how to rotate images of faces and how to extract eigenvectors to look at how the Pagerank algorithm works.
- 5 stars74.70%
- 4 stars19.73%
- 3 stars3.40%
- 2 stars1.14%
- 1 star1%
來自MATHEMATICS FOR MACHINE LEARNING: LINEAR ALGEBRA的熱門評論
Efficient, targeted course for learning the language and basic operations within linear algebra. Excellent for those working full-time, and for those without much experience with linear algebra.
Professors teaches in so much friendly manner. This is beginner level course. Don't expect you will dive deep inside the Linear Algebra. But the foundation will become solid if you attend this course.
Great way to learn about applied Linear Algebra. Should be fairly easy if you have any background with linear algebra, but looks at concepts through the scope of geometric application, which is fresh.
Great content and direction. Only negative is the sometimes frustrating experience with the Jupyter Notebooks: debugging what has gone wrong is very difficult, due to a lack of good error messages.