Machine Learning Flashcards
machinelearningflashcards.com
machinelearningflashcards.com
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.
I think these are fine if you are trying to review concepts while on a train or something. Emphasis on REVIEW and revisiting only, not learning.
“One of us has previously asserted (Michael Nielsen, Augmenting Long-Term Memory (2018)) that in spaced-repetition memory systems, users need to make their own cards. The reasoning is informal: users often report dissatisfaction and poor results when working with cards made by others. The reason seems to be that making the cards is itself an important act of understanding, and helps with committing material to memory. When users work with cards made by others, they lose those benefits.
Quantum Country violates this principle, since users are not making the cards. This violation was a major concern when we began working on Quantum Country. However, preliminary user feedback suggests it has worked out adequately. A possible explanation is that, as noted above, making good cards is a difficult skill to master, and so what users lose by not making their own cards is made up by using what are likely to be much higher-quality cards than they could have made on their own. In future, it’s worth digging deeper into this issue, both to understand it beyond informal models, and to explore ways of getting the benefits of active card making.”
I'd recommend these cards if they were higher quality than the ones I make if the person using the cards already read a textbook on the topics.
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.
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.
Not true, In my experience fitted SVMs have thousands of support vectors. The hinge loss is supposed to be less sensitive to outliers.
In general, I think SVMs are a 'terrible idea' because you can often get better fits at much faster run times with gradient boosting or you have to spend a lot of time getting the kernel just right.
But, knowing how to implement "X from scratch" gives you far more useful information about how and why a particular algorithm is suitable for a certain task. We should encourage people to reimplement algorithms from scratch (for fun, not production usage) so that they understand how the sausage is made.
Also, SVMs support a whole lot of different loss functions. I suspect that a properly tuned SVM will do better than a properly tuned logistic regression.
Notably they're not traditional flashcards where I can be given one side, answer the question or repeat the concept, and then check the other side for the answer. Everything is on one side.
Second, lots of different subjects which may generally be good for overall knowledge, but lots of random things I found more specific to data science than ML, which I didn't expect because they're called Machine Learning Flashcards. I do realize data science is kind of a proto ML though.
Third, I found them hard to read with all the colors, some of which didn't scan as well as I would have liked.
To be completely fair, I'm probably not the target audience, but I felt like I was marketed to as if I were.
IMO flashcards are more useful if you build them yourself and try to recall the context around it, not just what's in the flashcard. Doesn't have to be flashy, though flashiness may help with retention.
https://news.ycombinator.com/item?id=14897209
You guys hate gaming interviews more than gameable interviews.
Would love to hear any feedback you have. The app is called Notefuel: https://apps.apple.com/us/app/notefuel/id1458567718
I would love to hear from you guys when an Android app is available.
It's exactly how you create a program. You define functions using other, more lower-level, functions. The only problem with this approach is that it's tedious to create the cards. It might also be tedious for someone going through the cards.
What do you think?
For me that is a dealbreaker without even judging the merits of the product.