
A Few Useful Things to Know about Machine Learning [pdf] - alrex021
https://homes.cs.washington.edu/~pedrod/papers/cacm12.pdf
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hangtwenty
When I first read this paper, I found it to be immensely helpful. I'm new to
Machine Learning but I was so inspired by this paper when I first found it
that I wanted to build up resources around it.

Here's the result: [https://github.com/hangtwenty/dive-into-machine-
learning](https://github.com/hangtwenty/dive-into-machine-learning)

I want this guide to be a good resource for other people like me, who are
curious to get into Machine Learning by this process:

1) hacking 2) coming to understand what you hacked 3) more structured, in-
depth learning

It can be intimidating to approach Machine Learning this way. For a long time
it felt like I couldn't do steps 1 or 2... and had to start with 3. That's
intimidating!

Pull requests welcome, I want this to be a good resource! Thanks all.

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placebo
Well done - I'm sure this approach is helpful to many people who, like myself,
learn best by using the process you summarised, so here's another compliment
to the list :)

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paperwork
Pedro Domingos also has a fantastic mooc at
[https://www.coursera.org/course/machlearning](https://www.coursera.org/course/machlearning)

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hangtwenty
Whoa, I didn't realize this, thank you!

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bladecatcher
This is a very useful guide. Although I'd imagine that you'd have to have
atleast some experience with ML before you truly appreciate what's being
explained in the paper.

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hangtwenty
I'm assuming you meant to respond to me. Thanks! I agree with you, and I'm
sure it's riddled with mistakes. But, sometimes it takes a beginner to make a
beginner's guide. So I'm hoping it can be valuable in that way, and that
contributions can correct my mistakes.

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moridin
This is great, wish this was around about 6 months ago. I'm going to go over
this and fill in some knowledge gaps.

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dgmdoug
It was written in 2012 and is a fairly well known and accessible text! Pedro
is an excellent speaker, if you ever get chance to hear him I highly recommend
it.

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alfiedotwtf
Is anyone else getting an untrusted cert?

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alexmarcy
I got it too.

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alfiedotwtf
Ok cool. 2 hours into my comment, and I was beginning to think I was being
MiTM'd.

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sushirain
"So there is ultimately no replacement for the smarts you put into feature-
engineering."

Recently, deep learning changed this. Finding the right network architecture
allows the net to learn the features by itself.

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abrichr
That's the goal of representation learning, but we're not quite there yet.
From a previous comment:

syllogism 264 days ago

Deep learning needs feature engineering too.

You still need to transform your context into a vector of boolean or real
values, somehow. And that transform is going to encode assumptions about what
information is relevant to the problem, and what's not.

Let's say you're trying to predict house prices. There's no end of geo-tagged
data you might pull in. And if you have a cleverer idea than the next guy,
your model will be more accurate. And, probably, if the next guy's at least
competent, it'll be your feature ideas that set you apart.

In a linear model, you need to come up with a clever set of conjunction
features, that balances bias and variance. You don't need to do that for a
deep learning model, and that's a big advantage. But that's not the same as
saying there's no feature engineering.

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sushirain
Who said there is no feature engineering?

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nileshtrivedi
Just finished reading this. Brilliant!

