I would like to get more understanding of deep learning. Browsing the web I find applications in speech recognition and hand-written digits. However I would be interested to get some guidance on how to apply this in the classical setting:
- binary classifier
- numerical features (each sample is a numerical vector of $K$ entries, no 2D pixels or such).
I am doing my own experiments choosing learning rates, number of hidden neurons and so on, but I would be happy to see an application by somebody more experienced.
The software that I use offers weight initialziation using Restricted Boltzmann Machines (RBMs). I wonder whether this is useful in this context and whether the other special techniques that one encounters in the literature (convolutional NN) are useful here to.
Could anybody share a blog post, a paper or personal experience?