
A Guide to Deep Learning - adamnemecek
http://yerevann.com/a-guide-to-deep-learning/
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AndrewKemendo
The primary thing missing from all of these guides is that you need to have
two things for ML:

1\. A purpose for utilizing it

2\. A data set to train/act on

Without that, all you get are a bunch of shovels and picks, but no idea of
what kind of wood/bricks you need or a plan for the house.

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estsauver
I will mention that you can get surprisingly far with just a little bit of
labeled training data now. Transfer learning is a pretty powerful technique.

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1024core
You still need to train a model on what you're transferring _from_. It's not
like you can manufacture trained models out of thin air.

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AlexCoventry
I think the idea is that, for example, you start with some pre-trained visual
object-identification network, remove the final logistic-regression layer, and
replace with a layer which you train your problem on.

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nsxwolf
Everyone's into deep learning, but what would I actually _do_ with it? With
some other field, like computer graphics, one can fairly quickly get a 3D cube
spinning on their screen and know it has some relation to the special effects
in the Star Wars movie they just saw. No one makes it obvious what the
hobbyist can expect to do with deep learning or how it relates to the broader
world.

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malux85
Everyones into Graphics programming, but what would I actually _do_ with it?

The uses are myriad, you can build Star Wars Movies, you can build computer
games. You can do CAD and 3D modelling and printing, you can visualize fluid
simulations

But each is an entire field.

The same with Deep Learning -

You can do image classification for medical diagnosis. Self Driving Cars.
Realtime Translation. OCR. You can model chemical reactions by doing latent
space exploration. You can model gene-gene interactions. You can build a image
recogniser to tell if your cat is on the couch. You can build an AI that plays
GO, or any other system or game where the rewards are time-delayed and sparse.
You can make it play Atari games.

You can stitch satellite images together. You can reconstruct parts of photos
that are missing. You can colour black and white images. You can de-noise wind
sounds from microphones. You can search for comets. You can monitor
deforestation. You can count cars in car parks. You can search vast ocean
areas for survivors. You can build security drones that fly around at night
and look for anomalies. You can turn a webpage of unstructured text into
structured query-able forms (see named entity recognition). You can create
visual art (google: neural style transfer)

These are just a small drop of some of the cool things that are going on - and
while it might be difficult for you to advance state of the art without a GPU
cluster (just as it would be difficult to advance state of the art in Graphics
programming) the hobbiest can certainly start in any of these fields, just
pick one that inspires you.]

EDIT: I run a Deep Learning startup - if you want some pointers or help
getting started, or would like advice don't hesitate to email me - it's in my
profile :)

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gopher_protocol
One of my partner's tasks as a legal assistant is to go through mountains of
OCRed PDFs and classify them and extract pieces of data so that lawyers and
paralegals can go through them more easily. Do you imagine deep learning would
be an appropriate means of automating that, or is it overkill?

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malux85
No not overkill at all - I think the first step is to augment the human so the
machine helps them, then it can totally replace all of the laborious stuff as
time passes.

Imagine something like: A deep network reduces a 20 page document to a summary
of 4 or 5 sentences, you can click on these sentences to "expand" them out,
eventually getting to the original text. Saving them from reading the whole
document

A separate classifier automatically classifies the document into one of say,
20 categories (or whatever is appropriate).

A Deep Learning named entity recogniser extracts the Human names, Dates and
times, Email addresses, Company Names, email addresses, Money amounts, and
numbers from each document, then off to elasticsearch for indexing and easy
searching.

Then we can start to play with higher level legal concepts that (for example)
set precedent, or search for certain logical fallacies. (the next step past
machine learning is machine reasoning - and it's starting to be possible now)

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kowdermeister
If links are broken for you, then turn of your adblocker, because he is
measuring clicks with Google analytics and he's JS is broken thanks to the
missing _ga_ function.

#issue reported

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hrant25
thanks for reporting. It should be fixed now

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vhpoet
Hrant!! maloch, front page :)

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miguelrochefort
I'll bite.

We see these being posted every week. Why?

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hrant25
Many people (especially students from universities) keep asking me and my
coworkers at YerevaNN about good educational resources. They have different
levels of math background, some want to study theory and watch visualizations,
others want to play with the code before reading formulas..

At some point we understood it's better to spend some time and build a guide
that will cover most of these questions (and, as always, we spent a lot more
time on this than we expected)

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eanzenberg
I think OP is asking, why spend time writing another guide when there's plenty
that suffice, including full books and papers on the topic?

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hrant25
I was looking for something that would answer common questions for some time,
but couldn't find anything. Pointing students to research papers unfortunately
doesn't work in most of the cases. Deep learning book is too hard for some and
they want to see a comparison of different sources.

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hnarayanan
This looks really good, and an interesting wag to describe the landscape. Is
it just my phone or are the links completely broken on phones?

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otoburb
Confirmed that links are inactive on my phone as well, probably because they
don't seem to be prefaced with URI schemes (i.e. their [http://](http://) and
[https://](https://) prefixes).

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blueyes
this is by far the most visually attractive list of deep learning resources
i've seen. and they hit a lot of the main points.

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md2be
I didn't see k-means nearest neighbor in the list. Also, wouldn't a
mathematical statistics be a prerequisite?

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Eridrus
As far as I can tell, Deep learning in practice is pretty far ahead of the
theory, so people are largely following their intuition around rather than
being guided by theory.

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roye
I saw stars indicate difficulty; do colors also have some meaning?

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bhaumik
They contain resources from the same source. e.g. all the "green" content
comes from
[http://www.deeplearningbook.org/](http://www.deeplearningbook.org/).

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bamura
Good consolidation @adamnemecek...

