Example of binary classifier with numerical features using deep learning 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?
I used Binary classification for sentiment analysis of texts. I converted sentences into vectors by taking appropriate vectorizer and classified using OneVsRest classifier. 

On another approach, my words were converted into vectors and there, I used a CNN based approach to classify. Both when tested on my datasets were giving comparable results as of now.

If you have vectors, there are already really good approaches available for binary classification which you can try. On Binary Classification with Single–Layer
Convolutional Neural Networks is a good read for you for classification using CNNs for starters. 

This is one of the first blogs I read to gain more knowledge about this and doesn't require much of pre-requisites to understand(I am assuming you know the basics about convolution and Neural Networks).