Thanks! I'm really enjoying seeing the huge variety of domains people have applied char-rnn to. I haven't anticipated response at this scale, to both the original blog post and the code release. (I try to keep a somewhat up to date list of responses on the bottom of the blog - cooking recipes, music, obama speeches, magic cards...)
In terms of learning, I would also encourage people to try the materials from our CS231n class - this is a class I taught at Stanford last quarter with my adviser. It's technically about Convolutional Networks, but most of the class material is building up generic Neural Networks, backprop, and so on. You can also try our IPython Notebook assignments.
In terms of learning, I would also encourage people to try the materials from our CS231n class - this is a class I taught at Stanford last quarter with my adviser. It's technically about Convolutional Networks, but most of the class material is building up generic Neural Networks, backprop, and so on. You can also try our IPython Notebook assignments.
Course notes: http://cs231n.github.io/ Syllabus with slides too: http://cs231n.stanford.edu/syllabus.html
Another good pointer is Andrew Ng's Coursera class - that's a thorough introduction as well.