I think there's trade offs. I apologize if our marketing copy seems excessive, I'd love to fix that and take feedback seriously. Please do email me. Deep Learn

I think there's trade offs. I apologize if our marketing copy seems excessive, I'd love to fix that and take feedback seriously. Please do email me. Deep Learning done right applied to unstructured data is a great part of either an ensemble of methods or great for working with hard to engineer features. I think as for normal machine learning where you are typically doing feature engineering, you need to understand what's going on to make recommendations for actionable insights. Everything has its place. I'd like to echo Andrew Ng here, the hype in deep learning is overblown. While there are great results, it's not magic. Neural networks still need feature scaling, among other things to work well. Much of the hype comes from the giant marketing machine that are the PR firms for the research labs who need new data scientists. While great work is being done in these labs, much of it isn't going to be applicable in the day to day work of a data scientist just trying to do some some basic A/B testing. Hope that makes sense!