
Recurrent Neural Networks Tutorial, Part 1: Introduction - dennybritz
http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/
======
curiousjorge
can someone give me some real world business need where I can apply RNN and
this type of knowledge?

~~~
dennybritz
Author here. RNNs are used in the same places as most other Machine Learning
models and may replace some of the older models in the future. What these
models do is typically not visible to the end-customer (unless you sell
developer tools), but it's an important part of everyday technology. Just take
Siri for example. Under the hood Siri uses almost all of the application
mentioned in the post: Speech recognition, language modeling and (I think?)
machine translation. You can find parts of this in a lot of products that you
use every day.

In terms of business ROI, RNNs may not only yield better results for the end-
user, but may also lead to lower costs due to their simplicity. You may be
able to replace a very complex hand-engineered system with a RNN that's easier
and cheaper to maintain.

~~~
dxbydt
May yield... May lead...May replace. That's a lot of maybe. I submit that from
purely a business ROI pov, you are far better off building a 'very complex
hand engineered system' as you put it, since that's the natural outcome of
hiring say a bunch of rails/python devs at pennies on the dollar on some
offshoot dev portal. Building a nicely tuned scalable RNN model in the
industry requires a team with 100x intellectual capabilities for which you
will pay 100x, and there may not be a mature business case for that yet.Though
I agree much of these skills are being commoditized rapidly.

~~~
argonaut
Seems like a bit of a non sequitur. Nothing is guaranteed in life.

That being said, you've got this backwards. The natural outcome of hiring a
bunch of rails/python devs to fine tune a machine learning / translation /
recommendation system is that you get hundreds of thousands of lines of code
that run slowly AND don't work. The entire premise of "deep" learning is that
the system is a black box - features are learned by the black box. And you
typically use pre-rolled fast GPU implementations. Most importantly, _very
little_ domain specific knowledge is needed. In fact, getting that hand-
engineered system is going to be more complex, more costly, and it's going to
require people with more domain expertise.

