I've long had an urge to reimplement Metacat in JavaScript, so that people could play with it over the Web (Metacat, by Jim Marshall, is an evolution of Mitchell's Copycat). But as much fun as this would be, I'm not sure it will ever make it to the top of my priority stack.

http://people.idsia.ch/~juergen/

Cf. "Deep Learning in Neural Networks: An Overview" http://people.idsia.ch/~juergen/deep-learning-overview.html

> Abstract. In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarises relevant work, much of it from the previous millennium. Shallow and deep learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.