The Pros and Cons of Deep Learning

 
We will be discussing Deep Learning and related topics are our Big Data Conferences, training sessions and workshops.

Deep learning is a collection of statistical machine learning techniques used to learn feature hierarchies often based on artificial neural networks. That’s it. Not so scary after all.

For sounding so innocuous under the hood, there’s a lot of rumble in the news about what might be done with DL in the future. Let’s start with an example of what has already been done to motivate why it is proving interesting to so many.

Deep learning has been all over the news lately. In a presentation I gave at Boston Data Festival 2013 and at a recent PyData Boston meetup I provided some history of the method and a sense of what it is being used for presently. This post aims to cover the first half of that presentation, focusing on the question of why we have been hearing so much about deep learning lately.

What does it do that couldn’t be done before? We’ll first talk a bit about Deep learning in the context of the 2013 kaggle-hosted quest to save the whales1 The game asks its players the following question: given a set of 2-second sound clips from buoys in the ocean, can you classify each sound clip as having a call from a North Atlantic right whale or not?

The practical application of the competition is that if we can detect where the whales are migrating by picking up their calls, we can route shipping traffic to avoid them, a positive both for effective shipping and whale preservation.

The content is aimed at data scientists who might have heard a little about deep learning and are interested in a bit more context. Regardless of your background, hopefully you will see how deep learning might be relevant for you. At the very least, you should be able to separate the signal from the noise as the media hype around deep learning increases.

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