In a previous post I discussed the promising applications for deep learning in the enterprise. The greatest potential for deep learning is in adding business-relevant structure to less-structured, sense-like data — such as images, audio and other sensor data.
How quickly does the tone and affect of a support call from a frustrated customer change, broken down by support rep? It’s that time-to-mollification that matters to your business, not the raw sound data.
Generally when training machine learning algorithms (and deep nets are an extreme example of this), the more data the better. There’s a persistent danger of “overfitting” your data — performing very well on the training set, but poorly on new data. If the algorithm has overfit, it has failed to generalize and is thus not that useful.