
Ask HN: How to start with maths required for ML or Deep Learning? - n13
I really want to learn about Machine&#x2F;Deep Learning. I tried to start with some ML courses and online resources but I got intimidated when I saw that it required really good background in Maths. I do have basic intro to Calculus, but I don&#x27;t know much. It seems to get really good at ML, you need to know a lot about Maths. I&#x27;m sure some of you have already crossed this hurdle, so I&#x27;m really interested to learn about your experience. I did google search and encountered this link[1] but by looking at the resources, it seems that it&#x27;s a lot of ground to cover. I feel overwhelmed, so I&#x27;m just looking to cover the minimal ground.<p>[1] https:&#x2F;&#x2F;www.quora.com&#x2F;How-do-I-learn-mathematics-for-machine-learning
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chollida1
I wrote this comment a while ago but I think its still very relevant...

I wrote about this here:
[https://news.ycombinator.com/item?id=8767092](https://news.ycombinator.com/item?id=8767092)
and here:
[https://news.ycombinator.com/item?id=9433316](https://news.ycombinator.com/item?id=9433316)

Long story short, the biggest mistake I see people making is not actually
rolling up their sleeves and learning the math.

People are often content to watch hour after hour of Udacity, Khan academy and
Coursera videos but the applied follow up is where most people drop off. At
the very least any course work should be followed up by something practical
like a kaggle exercise to prove that you can apply the technique you just
learned. Consider the benefit of just watching videos vs doing actual applied
work.

On one hand if you just watch videos you might learn alot but how do you prove
that to someone hiring you? On the other hand if you sit down and spend a week
attaching a Kaggle excise then at the very least you have something to point
people to, to show that you can apply machine learning techniques.

My recommendation has always been to read the first 5 chapters of Introduction
to statistical learning: [http://www-bcf.usc.edu/~gareth/ISL/](http://www-
bcf.usc.edu/~gareth/ISL/)

and if you fly through it then sample Elements of statistical learning
[http://statweb.stanford.edu/~tibs/ElemStatLearn/](http://statweb.stanford.edu/~tibs/ElemStatLearn/)
for the topics that you want to learn.

If intro to statistical learning is too advanced, then go to Khan academy and
work your way through their statistics videos. From my experience you can
bucket people into skill level by looking at how they attack a new problem.

Beginners tend to start by saying they'll need a hadoop cluster and spend the
next week setting up a pipeline.

Intermediate people tend to jump into R or scikit and try to model the problem
with a small subset of data and the library and technique they know best. The
advanced people tend to flesh out their hypothesis first and then work out the
math and then jump to modelling with a small set of data and finally move to a
cluster.

~~~
Jugurtha
>Beginners tend to start by saying they'll need a hadoop cluster and spend the
next week setting up a pipeline. Intermediate people tend to jump into R or
scikit and try to model the problem with a small subset of data and the
library and technique they know best. The advanced people tend to flesh out
their hypothesis first and then work out the math and then jump to modelling
with a small set of data and finally move to a cluster.

This is funny. It all boils down to metacognition, I suppose. Beginners don't
know how much they don't know; they're seeing the tip of the iceberg but don't
know the concept of an iceberg to begin with. It's just that white thing over
there.

Intermediates see the tip of the iceberg and slightly panic while trying to
correct course.

Advanced know they're in the polar circle because they know geography, they
plot their course because they know navigation, and actively look-out for
icebergs.

Here's what excites me.. The term "Emerging country" was used so much, that
the real meaning of the concept of "Emergence" is practically unknown.

Here's the first paragraph from Wikipedia:

>In philosophy, systems theory, science, and art, emergence is a process
whereby larger entities, patterns, and regularities arise through interactions
among smaller or simpler entities that themselves do not exhibit such
properties.

Something that's greater than the sum of things that constitute it, but it's
still constituted by those very things.

My point is that often times, you'll find "resources" or tutorials that try to
hide all the yak shaving that's necessary to get into a field (the
constituents) and try to give you the "fruit". I'd much rather a course that
says: here are the prerequisites for this course, here's what you _need_ to
know already, if you don't, go learn that and come back because you'll only
waste your time. Listing exactly the things one needs to know would save time,
in my opinion.

------
GFK_of_xmaspast
To quote a mathematician you might have heard of, "μή εἶναι βασιλικήν ἀτραπόν
ἐπί γεωμετρίαν", that is, "there is no royal road to geometry", that is,
there's no easy way to get there without doing it.

~~~
throweway
There is certainty no royal road to type theory. Ugh!!

------
curuinor
This may not be the answer you were hoping for, but if you do not have the
background already to do this sort of thing and if you are not already
stubborn enough to start learning mathematics on your own, school may be for
you. I assume you are not stubborn enough to start doing so because you have
not done so already.

A short master's (3-4 semesters) is about enough to have all the math
background + some application classes.

------
kafkaesq
Yes, it's a lot of turf to cover. Until very recently, most of it was at best,
barely touched on in a typical undergraduate curriculum. But here's one source
you'll see cited a lot:

An Introduction to Statistical Learning

[http://www-bcf.usc.edu/~gareth/ISL/](http://www-bcf.usc.edu/~gareth/ISL/)

------
tgflynn
To understand ML you'll at least need a good understanding of vector
differential calculus and linear algebra. There are many free ressources
available for learning math today, MOOC's, free textbooks, etc. but if you
were studying this in college it would amount to at least 2 semesters of
courses that many people find quite challenging. So while you can probably
learn this on your own it will likely require quite a significant time
commitment as well as strong self-discipline.

You may be able to make some use of existing ML models and libraries without a
deep understanding of the methods however.

~~~
e19293001
> it will likely require quite a significant time commitment as well as strong
> self-discipline.

Just get a textbook that appeals to you and spend most of your time reading it
and focus. If it does not work, get another textbook that appeals to you and
spend most of your time reading it and focus. If it does not work, get another
get another textbook that appeals to you and . . .

Really. Spending time studying a textbook is what you need. In the end, you'll
realize how you become mathematically mature.

