Humans and animals learn much better when the examples are not randomly presented but organized in a meaningful order which illustrates gradually more concepts, and gradually more... Watch Video

Whereas theoretical work suggests that deep architectures might be computationally and statistically more efficient at representing highly-varying functions, training deep architectures... Watch Video

n order to tackle large-scale learning problems whose solution necessarily involves a large model with many tunable parameters, difficult non-convex optimization has to be performed... Watch Video

We describe a new approach for modeling the distribution of high-dimensional vectors of discrete variables. This model is inspired by the restricted Boltzmann machine (RBM), which... Watch Video

This short tutorial on deep learning will review a variety of methods for learning multi-level, hierarchical representations, emphasizing their common traits. While deep architectures... Watch Video