Prerequisites - Linear Algebra
Before the workshop: this deck checks the linear algebra that Tensors for Machine Learning builds on: vectors and matrices, products and transposes, inverses and linear independence, norms, special matrices, eigenvalues, the SVD, the pseudoinverse, the trace, the determinant and PCA.
It follows Chapter 2 of Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville, free to read at deeplearningbook.org. Read the chapter there and come here to check what stuck: every card cites the page its answer comes from.
The same questions make up the workshop’s intake test, so your instructor can see where the group starts.
Choose how to study
Flash Cards
Flip between question and answer, in both languages, at your own pace.
Rapid Repetition
Short sessions that resurface exactly what you forget, until it sticks.
Practice Test
Exam-style questions with a score at the end. Find your weak spots.
Quiz Game
Beat the clock on multiple-choice questions. Anything you miss comes back later in the round, until you know it.
Explore the topics
Read it all?
Rapid repetition replays exactly what you forget — the fastest way to lock it in before the exam.
Companion questions for Chapter 2 of Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville (MIT Press, 2016), deeplearningbook.org. Summaries and questions are original; quoted passages are cited to the book.