Jeremy Howard goes super practical on the missing piece in Ng's course covering a topic that is, for many classical problems, the best solution out there. Fast.ai's approach is what is called Top-Down, meaning they show you how to solve the problem and then explain why it worked, which is the total opposite of what we are used to in school. Jeremy also uses real-world tools and libraries, so you learn by coding in industry-tested solutions. The reason why we are all here, Deep Learning! Again, the best resource for it is Professor Ng's course, actually, a series of courses.

These six brief videos, recorded in 2020, contain ideas and suggestions from Professor Strang about the recommended order of topics in teaching and learning linear algebra. The first topic is called A New Way to Start Linear Algebra. The key point is to start right in with the columns of a matrix A and the multiplication Ax that combines those columns.That leads to The Column Space of a Matrix and the idea of independent columns and the factorization A = CR that tells so much about A. With good numbers, every student can see dependent columns.The remaining videos outline very briefly the full course: The Big Picture of Linear Algebra; Orthogonal Vectors; Eigenvalues & Eigenvectors; and Singular Values & Singular Vectors. Singular values have become so important and they come directly from the eigenvalues of A'A.You can see this new idea developing in the first video lecture of Professor Strang’s 2019 course 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning.