This is more or less just a random collection of links I've come across while researching Deep Learning, I hope they are as useful to you as to me.
[cached]Neural Networks, Manifolds, and Topology is a great post on how to visualize deep neural networks and get an intuition for them. Reading it was the first time I truly appreciated how the successive layers of NNs just transform the topology of the input data, until finally it becomes linearly separable.
[cached]A Gentle Introduction to Backpropagation provides an unusual (but maybe easier to understand) look at back propagation, the essential algorithm for updating weights when training neural networks.
[cached]ImageNet Classiﬁcation with Deep Convolutional Neural Networks is a very interesting landmark paper on the use of Deep Learning in Computer Vision.
[cached]Visualizing and Understanding Convolutional Networks helps immensely to understand how a convulutional network represents its features and how it generalizes in successive layers.