Machine Learning Flashcards

This is not a good idea and definitely not worth the money.

If you want to pass a quiz-style interview (I interview for a FAANG ML research lab), you are much likely better served writing down the concepts yourself in a concise way. If you never wrote them down somewhere, you are not forced to actually digest the content. Cards you didn't write yourself will fool yourself into believing you understand something if you can repeat the words.

Agree and disagree. Memorizing these will do very little. On the other hand having an aggregate list of topics that you should know that you use to point you in the direction of further learning could be useful. Like a table of contents for a book you have to put together yourself. Not useful on it's own, but is a good indicator of where you should be going.

This isn’t how you learn complex topics. This is how you memorize sound bites.

There are people saying here that is not worth it. I tend to agree but for different reasons. Flashcards with spaced repetition can help you learn mathy concepts, as Michael Niesel and others have shown.

As high_derivative notes, to make the process worthwhile, you need to make the notes yourself in order to internalize chunks that are worthwhile to you. That means your own definitions that you are failing to remember, the questions you need to answer, the problem sets you want to review, etc.

You can only internalize with a method like flashcards w/ spaced repetition once you understand the argument, need, and narrative.

This is encouraging exactly what I dislike about the current ML-in-industry space, namely the fetishism surrounding being able to describe the rote steps of an algorithm and all this "X from scratch" stuff. It's good to know "this algorithm is used for binary classification" but there are so many subtleties to how the data is reckoned with through these algorithms and how that particular representation of the problem maps onto your current business task.

For instance, I'm doing a project that involves binary classification but I already know that linear SVMs would be a terrible idea because the hinge loss only focuses on two data points and essentially ignores all the rest. Logistic regression is much more appropriate for my needs because it is directly optimizing the estimates of probability of belonging to one class or the other, by virtue of that literally being the definition of the objective function. This, though, doesn't really sink in without significant practical experience, and definitely wouldn't stick if it was recited to you from the front of a lecture hall or one of a couple hundred flash cards.