This form of machine learning continues to grow rapidly. Here's what you need to know as you consider whether to implement deep learning in your organization.
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1. Deep learning is useful only for complex problems.
As is common with emerging technologies, people have inflated expectations of what deep learning can do. Deep learning is very powerful, but it requires vast amounts of resources. And it is overkill for some basic analytics problems.
In its Deep Learning Guidebook, data science platform vendor Dataiku compares deep learning to traveling by airplane. It's great if you want to go from New York to Paris. But if you want to go from Manhattan to Brooklyn, taking a plane just doesn't make sense.
In the same way that air travel is best for long distances, deep learning is best for complicated problems. It doesn't require deep learning to predict that someone who bought ice cream cones might also want to buy ice cream. Human data scientists are good at understanding those sorts of problems and building models that can help them predict purchase behavior. In fact, regular machine learning is better for most structured data, that is, the kind of data that can reside in a traditional database.
However, if you weren't sure how to build a model for a complex problem that involves unstructured data — like determining which Facebook posts have "fake news," which images show the earliest signs of cancer or which network traffic is malicious — that's where deep learning might become helpful.
Cynthia Harvey is a freelance writer and editor based in the Detroit area. She has been covering the technology industry for more than fifteen years. View Full Bio
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