
Practical Deep Learning for Coders - rspivak
http://course.fast.ai/
======
joshgel
I took both of these courses and recommend. Practical is the key word in their
description that differentiates them from other courses. The focus is on
getting you up and running ASAP, then explaining the details and variation,
then finally introducing you to learning how to translate the newest papers
into usable code. You're not going to get a PhD from the course, but it will
get you started on real-life projects.

~~~
jszymborski
You might get an MSc, however :P Half of my master's thesis was based on
practical understanding and skills I learned from the first Fast.ai course.
It's also inspired me to start a PhD in a more quantitative field to persue
probabilistic models in medicine.

(Here's that's master's thesis if you're interested :) Warning, big PDF ahead:
[https://www.cs.mcgill.ca/~jszymb/thesis/260528685_Szymborski...](https://www.cs.mcgill.ca/~jszymb/thesis/260528685_Szymborski_Joseph_Experimental_Medicine_thesis.pdf))

~~~
alexcnwy
Same here! Still busy with my MSc thesis though because I'm too busy applying
some of the other stuff I learned from Fast.ai on other projects ':)

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collyw
Ok, from my understanding deep learning is actuially a fairly complicated
subject, a bit more than learning the JavaScript framework of the month.

So is there any point in treating it as such? Just another skill we should all
learn superficially, so that we have a lot of very shallow knowledge?

~~~
minimaxir
It depends on your goal. If you want to make advancements in the field and
find innovations that no one else has done (e.g. GANs and Reinforcement
Learning), then a superstrong mathematical background is helpful. But if you
just want to _find the best output given a set of inputs_ and tune things for
business needs, modern tooling like sklearn and TensorFlow/Keras is more than
sufficient, and following a set of modern data transformation and model
heuristics can get you 80% of the way there.

Medium thought-pieces/YouTubers conflate both perspectives, which has become a
problem.

~~~
fizwhiz
_cough_ siraj raval _cough_

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kevmo
Some of the best instruction I've ever received (and I have too many degrees).
These courses are intensely practical. Making sure people understand the
theoretical underpinnings comes second to making sure people can actually use
DL/ML software for their own purposes.

The site could use a reorganization, though. Jeremy and Rachel have created a
number of courses, but you have to hunt through their YouTube and fast.ai
forum comments to find all of them. So much good content should not be buried!

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geekfactor
If you're looking for a group to take this course with (Jeremy recommends it
[1]), we just started a Fall session of the TWiML Online Meetup's Fast.ai
study group. More info here:
[http://twimlai.com/fastai](http://twimlai.com/fastai)

[1]
[https://twitter.com/jeremyphoward/status/996445183456690176](https://twitter.com/jeremyphoward/status/996445183456690176)

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avinassh
What are the math prerequisites? Couldn't find anything in about page [0], all
it says is:

> We assume that everyone taking this course has at least one year of coding
> experience.

so, no math background required? Would really appreciate an input from someone
who has done the course

[0] - [http://course.fast.ai/about.html](http://course.fast.ai/about.html)

~~~
jacurtis
Maybe just start watching it if you are interested in learning, instead of
trying to put yourself in a box that you might not have the math skills needed
for it.

It is free, you have nothing to lose. If the topic interests you than I am
sure you will figure it out. You can always google a math concept that
confuses you. There isn't a test and no one will even know.

~~~
pmulv
> It is free

Just as a warning for anyone price-sensitive interested in taking this course,
its cost is non-zero due to cloud costs. It is free to audit though! I'm not
trying to dissuade anyone; it is just good to know going into it.

~~~
abraham_lincoln
I did manage to setup the notebook and cuda on my local machine. I have an
older GTX card.

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mooneater
> we teach "top down" rather than "bottom up". For instance, you'll learn how
> to use deep learning to solve your problems in week 1, but will only start
> to learn why it works in week 2

That sounds more like bottom up to me (practice before theory).

~~~
scabarott
I think they mean it in the sense that:

Theoretical _foundations_ \--> practical problems = Bottom Up,

practice without theory --> building up the theoretical legs to stand on ==
Top Down

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adamnemecek
Has anyone here actually built an ml product? A product people use?

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minimaxir
That's setting the goalposts a bit narrow in terms of practical applications
of ML. You can apply deep learning to solve problems aren't public facing,
such as analytics forecasting.

Additionally, a lot of public ML products don't use, and don't _need_ to use,
deep learning (e.g. NLP applications).

~~~
friday99
Analytics forecasting doesn't really seem like a good candidate for deep
learning. There are plenty of established methods for forecasting that are
simpler, more robust and generally more effective in terms of effort to
reward.

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master_yoda_1
I don’t understand the big deal about state of the art result. If you know the
algorithm (from paper) and the high level tool is available (pytorch) then it
is not difficult. If you find it difficult then you are not a good enough
software developer and you are not able to produce anything substantial. With
fast ai you would mostly end up burning energy in gpu and waste your time. I
would better learn c++ then deep learning.

