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

"We are currently working on a much more interesting set of training melodies and chords". More like "my postdoc ended in Switzerland and I started a faculty job at University of Montreal (LISA lab) and never had time to get back to LSTM and music composition. Sigh.

I think there's something right about that idea. It seems, to me at least, that this idea of storing and re-using motifs with variation is at the heart of improvization. (Author of paper).

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.