
Facebook Field Guide to Machine Learning – video series - tosh
https://research.fb.com/the-facebook-field-guide-to-machine-learning-video-series
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minimaxir
Facebook's guide comes a couple weeks after (EDIT: FB's guide was originally
published on May 7th, so it's actually a few months old) Google published
their ML guides
([https://news.ycombinator.com/item?id=17595611](https://news.ycombinator.com/item?id=17595611)).

That's not a bad thing; the more guides from reputable sources, the better.
Just don't read them and say you're an ML expert afterwards.

~~~
lazybreather
Well then. Your 2 cents on how to become one after going through those videos?
I am genuinely asking. Looking for a career in it. At the beginning now. Read
a fair bit, basics videos and all that online. Nothing formal. Can do a bit of
c, c++.

~~~
EpicEng
Like anything... do it. If you have the academic background get an entry level
job. If not, you'll have to build things using ML.

~~~
minimaxir
Of note, there aren't really any entry-level ML jobs (Data Analyst isn't the
same, although would be a valid stepping stone).

~~~
nl
Sure there are.

I get freelancers to do data preprocessing for me frequently, and sometimes
put it through some off the shelf model.

Generally it's hard to find the right people for this, but that isn't exactly
unique.

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ThePhysicist
I read the summaries and skimmed through the videos but couldn't find anything
on ethics, fairness, transparency or data protection. Is there anything?

If not, I really think that these topics should be addressed (even if only
briefly) in any "field guide" to machine learning. Especially FB should give
those some more attention after their numerous scandals and "mishaps".

Now, before you downvote this or say "but this is just about the methods and
tools" please take a moment to think about how much power ML models can have
over people when deployed at FB scale.

I would like to see at least a (brief) discussion of the following topics in
such guides:

\- Data Provenance & Data Ethics (Can I use this? Should I?)

\- Data Protection & Security (How can I protect personal information when
doing ML?)

\- Model Fairness (How can I ensure my model is fair and does not
discriminate?)

\- Model Transparency (How can I explain the results of the model to users and
colleagues?)

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amrrs
This has become a new CSR-type trend (esp. GOOG vs FB). Steps: * Open Source
ML Tools and Press covers it

* Create a video Series about ML and a blog post about it

Thus make people believe:

* You are a leader in ML

* You care about democratising ML or AI whatever

* You care about sharing knowledge

* You care about Open Source

~~~
wyattk
Look, I don't much care for Facebook as a company or as a product. FAIR is a
bit separate and does a lot of pure research. FAIR has put out a lot of good
work, tools (it supports PyTorch), and has a lot of good people working there
with good intentions.

Not everyone that works for a company that a given person dislikes is "evil"
or acting malevolently. No company is homogeneous.

~~~
visarga
Yep, FAIR is good for the ML community. They have a number of open source
projects that are useful.

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gruglife
Does anyone else find it old that the best engineers are basically selling ads

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Eridrus
Most engineers at large Ad-supported companies are not working on ads in any
meaningful way.

Ads pay the bills, but only to the extent that you have something people want,
to put the ads next to, and most of the engineers are there trying to make
something people want.

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Bobbleoxs
I'm half way through Google's crash course on ML which I think is helpful. As
a machine learning field guide, I also find Andrew Ng's short paper series
Machine Learning Yearning helpful. I watched the first FB video and didn't
feel like they added anything particularly interesting. It's almost like they
feel obliged to put something out there under the FB name.

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kumarvvr
I am an electrical engineer interested in Machine Learning applications.

So far, based on the myriad of literature I have read about Machine Learning
stuff, a lot of results and their quality depends on the type of network,
training, error correction methods, etc.

Say I learn how to make computers build and train models, but is that enough
to get good results? Are there any resources that will guide me into choosing
a good topology or Network parameters (say like number of hidden layers, etc)

How do developers who use Machine Learning in production environments
confidently come to a particular network topology/parameter set for a given
class of problem?

~~~
charlysl
Do Caltech's free online course "Learning from data" to learn ML in a
principled way. You will get the answers to all those questions and then some.
You basically need to learn some basic theory and concepts (linear models, non
linear transformations, vc dimension, learning curves, regularization,
validation, etc) much more than tools first to deal with those questions. The
professor in the video lectures is excellent

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jsonne
More practically Facebooks ads run off ML at this point meaning you need to
feed them a ton of data to get decent results. It's good to learn their take
on it for advertisers.

~~~
cm2012
As an advertiser, this! Lookalikes and optimizing for conversions are the key
to effective FB advertising at scale.

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kanodiaayush
Is it just me or does it feel like these are very generic ideas floated in the
videos, and that concrete examples would have helped a lot more?

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pandasun
Interesting how this kind of news always drops whenever there is bad press
about Facebook.

