
Machine Learning Flashcards - rgardaphe
https://machinelearningflashcards.com/
======
SpaceManNabs
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.

~~~
gmccreight2
In a more recent article Michael Nielsen and his co-author Andy Matuschak have
adjusted their thinking somewhat:
[https://numinous.productions/ttft/](https://numinous.productions/ttft/)

“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.”

~~~
SpaceManNabs
Thank you for this. I just want to emphasize that you still need to understand
the reasoning and ideas behind ideas. First rule of supermemo still applies:
Do not learn if you do not understand.

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.

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high_derivative
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.

~~~
admiral33
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.

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uoaei
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.

~~~
autokad
> "VMs would be a terrible idea because the hinge loss only focuses on two
> data points and essentially ignores all the rest"

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.

~~~
uoaei
I'm eschewing kernels entirely and just sticking to linear models for reasons
around interpretability (need to convert the model's coefficients+intercept to
an explicit Boolean statement). But you're right that kernel methods are more
flexible (maybe too flexible).

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chrisalbon
My biggest problem is that Chris is an asshole.

~~~
jbo1984
I sensed that listening to his podcast all those years. lol

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theptrk
Drawing these illustrations are super helpful for info absorption while
learning. I drew these while taking the Andrew Ng Coursera ML course.
[https://theptrk.com/2020/02/12/notes-for-coursera-ml-
course-...](https://theptrk.com/2020/02/12/notes-for-coursera-ml-course-
week-1-5/)

------
ganstyles
I purchased them in 2019 as I was refreshing my general ML knowledge as I was
getting deeper outside my particular ML specialty at work. Loaded them into
Anki. Wasn't really a fan, but definitely one of those "$12 isn't worth
complaining about it and getting a refund from someone who seems earnest" type
of situations.

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.

~~~
thesausageking
Thanks for sharing your experience. These don't sound like what I would call
"flash cards" if they only have material on one side. And I'm not sure the
point of loading them into Anki.

~~~
ganstyles
Well, I assumed they were because they're literally called "Machine Learning
Flashcards" and, being what I assumed we're flashcards, I loaded them into
Anki, which is for help memorizing concepts.

~~~
elbear
Well, Anki is mentioned on the landing page, so I assumed they're that kind of
flash cards too.

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alexilliamson
These come up a lot on data science twitter. I never understand who the target
audience could possibly be.

~~~
benrbray
Yeah, this seems like a terrible way to actually learn ML. Maybe it's for
business-type people who want to stay up to date on the buzzwords?

~~~
DelightOne
What are the good ways to learn ML?

~~~
kd5bjo
Practice. Get some data set and try to make a classifier according to the
various techniques.

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starpilot
Very similar negative comments as with InterviewCake:

[https://news.ycombinator.com/item?id=14897209](https://news.ycombinator.com/item?id=14897209)

You guys hate gaming interviews more than gameable interviews.

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bsanr
Darn, got my hopes up. [https://www.evernote.com/l/ABq8HaHnNtlBRJNb62FXRx-
imGp-s0-jY...](https://www.evernote.com/l/ABq8HaHnNtlBRJNb62FXRx-
imGp-s0-jYPg/)

~~~
notefuel
We recently launched precisely this - a note-taking app that lets people add
questions/identify salient terms for review as flashcards, and ML that applies
those questions to auto-generate fill-in-the-blank for any written material.

Would love to hear any feedback you have. The app is called Notefuel:
[https://apps.apple.com/us/app/notefuel/id1458567718](https://apps.apple.com/us/app/notefuel/id1458567718)

~~~
bsanr
Thank you, this is very close to what I was hoping for. I tested it out, and
if I were in a position to pay for the premium features, I would highly
consider it. However, the biggest stopgap for me is that my daily driver phone
is a Samsung Note8, which of course means that I'm unable to use NoteFuel for
the kind of spur-of-the-moment note-taking that would allow me to transition
from Evernote (or else simply add it to my routine).

I would love to hear from you guys when an Android app is available.

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

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elbear
One way you could use flash cards to teach a subject is by defining each
concept on its own flash card. You start from low-level concepts or primitives
as they're called in programming languages and use those to define more high
level concepts.

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?

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mauliknshah
I always feel forgetting the equations and algorithms, and I think this thing
would help.

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bigdict
These are great, thank you for posting!

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sabujp
these will help people fake it till they make it

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threeseed
You wrote your name on every single card ?

For me that is a dealbreaker without even judging the merits of the product.

