[douglaseck]

Author (http://research.google.com/pubs/author39086.html) of the paper here. I'm amused this is on Hacker News. The goal was to learn very long-timescale limit cycle behavior in a recurrent neural network. The chord changes are separated by many intervening melodic events (notes). As it turns out, even LSTM is pretty fragile when it comes to this. One problem is stability: if the network gets too perturbed, it can move into a space from which it never recovers. I'm not all that proud of the specific improvizations from that network, but I did enjoy learning what's possible and impossible in the space. I think now, with new ways to train larger networks on more data, it's time to revisit this challenge.

Edit: Formatting. I clearly don't post much on HN.

[cwhy]

Hi Douglas, I just finished my college and quite interested in RNNs and fascinated by their capability and potential. Should I go to graduate school to study it or I can play with it as a hobby. Do you have any suggestions?

[douglaseck]

I think you could play around as a hobby. You might try Theano as a place to start (for LSTM: http://deeplearning.net/tutorial/lstm.html). If you become passionate about neural networks you might find yourself in grad school simply because that's a great place for diving in more deeply. It's really really helpful to know machine learning. Andrew Ng's Coursera is a great place to start: https://www.coursera.org/course/ml