
A curated list of resources dedicated to recurrent neural networks - adamnemecek
https://github.com/kjw0612/awesome-rnn
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thewhitetulip
Almost daily something about Deep learning/ANN/RNN is on the front page.

I have only recently begun to learn this I do not find a nice starting point
like we have for other fields like a bunch of books etc

I read a lot of books, a lot of video lists and finally settled down on
Georgia Tech ML class ud262 which is awesome and just plain theory.

I want to build an AI to manage my day, any pointers as to where I shall start
looking into when I am aware of theory concepts?

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MichaelBurge
Thanks for posting this. These are going to be really helpful for me. I've
been trying to learn mostly from Karpathy's reference code for the past week,
and these resources should give more reference material.

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billconan
can recurrent neural networks be successfully applied to stock market?

I don't seem to see a single paper in the finance area.

~~~
dsacco
To your first question: yes, for some definitions of
"successfully"[1][2][3][4]. It's difficult, and it isn't as though you train
your algorithm and watch it make money. It's more that some functions of data
analysis become incrementally easier or superior. It's fundamentally similar
to applying RNNs to any other signal processing problem.

To your (implied) second question: tech companies like Google, Facebook and
Microsoft have a strong incentive to publish their researchers' work. It's
good for recruiting and pushing progress forward overall. Financial companies
have a strong incentive _not_ to do this, in fact they are incentivized to be
secretive. In general this means the only quant finance papers you see are
published by academics, not quants. This typically results in fewer papers
with stunning results from the financial industry, whereas we're almost
getting progress fatigue from the papers published by tech companies (where
"stunning" means impressive and maybe immediately useful).

[1]: [https://arxiv.org/pdf/cond-mat/0304469.pdf](https://arxiv.org/pdf/cond-
mat/0304469.pdf)

[2]: [http://cs229.stanford.edu/proj2012/BernalFokPidaparthi-
Finan...](http://cs229.stanford.edu/proj2012/BernalFokPidaparthi-
FinancialMarketTimeSeriesPredictionwithRecurrentNeural.pdf)

[3]: [https://clgiles.ist.psu.edu/papers/MLJ-
finance.pdf](https://clgiles.ist.psu.edu/papers/MLJ-finance.pdf)

[4]:
[http://www.kolegija.lt/dokumentai_img/Maknickiene-3-8-Pages-...](http://www.kolegija.lt/dokumentai_img/Maknickiene-3-8-Pages-
from-IITSBE-2011-2-Nr11.pdf)

~~~
billconan
Thank you!

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calebm
Every time I read "curated list" I think to myself, "isn't every list
curated?" Some lists are curated by humans, and others are curated by software
algorithms.

~~~
eriknstr
Imagine a list of RNN resources curated by a RNN.

~~~
calebm
If you believe they're really effective, then you should believe in that list.

