yup. this is the first time I understood someone from this field. Honestly, this dude just broken down the wall.
What's more important, passion flows through his writing. And it can be felt. I got so excited while reading it.
As a bonus, there's an ongoing class on deep learning architectures for NLP which covers Recurrent (and Recursive) Neural nets in depth (as well as LSTM's and GRU's). Check out cs224d.stanford.edu for lecture notes and materials. The lectures are definitely being recorded, but I don't think they're publicly available yet.
His username is badmephisto if you're interested.
(a) He seems to be very intelligent. Kudos. But…
(b) How good of an idea is it really to create software with these abilities? We're already making machines that can do most things that had once been exclusive to humans. Pretty soon we'll be completely obsolete. Is that REALLY a good idea? To create "face detectors" (his words!)?
Our relevance is ephemeral, but our influence will be lasting. Do we want to have a legacy of clinging to our personal feelings of importance, or of embracing the transience of our existence and nurturing our (intellectual) progeny?
If you enjoyed his blog posts, I highly recommend watching his talk on "Automated Image Captioning with ConvNets and Recurrent Nets"[2]. In it he raises many interesting points that he hasn't had a chance to get around to fully in his articles.
He humbly says that his captioning work is just stacking image recognition (CNN) on to sentence generation (RNN), with the gradients effectively influencing the two to work together. Given that we've powerful enough machines now, I think we'll be seeing a lot of stacking of previously separate models, either to improve performance or to perform multi-task learning[3]. A very simple concept but one that can still be applied to many other fields of interest.
[1]: http://cs231n.stanford.edu/
[2]: https://www.youtube.com/watch?v=xKt21ucdBY0
[3]: One of the earliest - "Parsing Natural Scenes and Natural Language with Recursive Neural Networks" http://nlp.stanford.edu/pubs/SocherLinNgManning_ICML2011.pdf