6 practical deep learning examples for today’s data-driven business

By | 2 minute read | June 15, 2016

Data is created and consumed so quickly that we can’t even nail down the statistics in this blog post (watch in real-time via Internet Live Stats). The term ‘big data’ is hardly still accurate – this is massive data. And the vast majority of it is unstructured — data (such as emails, tweets, photos, videos, articles, etc.) intended for human consumption and not designed for computers to process.

Over the past several years, businesses dealing with tremendous amounts of data have shifted their focus. Time that was once dedicated to poring over charts, tables, and spreadsheets is now spent seeking intelligent ways to automate data analysis and connect the dots between what consumers are saying across all channels. This transition to helping computers think and learn like humans is known as deep learning and it is happening because of the wide availability of technologies that are faster, adaptable and more accurate.

Today, deep learning is virtually everywhere. It’s on Amazon and Netflix making personalized recommendations. It’s on your smartphone helping your voice-activated assistant understand you. It’s helping websites and mobile applications transform content into precisely targeted advertising. It’s helping companies gain meaningful insights from unstructured data. It’s even helping Bob Dylan write new lyrics.

Deep learning is everywhere, but there is still a huge opportunity for businesses to capitalize on this technology. Fortunately, cognitive computing services are moving from research labs to organizations who want to solve industry challenges, stand-out from the competition, and stay ahead of customer demands.

“We’re seeing companies leverage unstructured data—things like photographs, videos, chat logs, documents—to make better, more informed business decisions to automate processes. They’re leveraging human-like capabilities inside automated workflows with deep learning. I think that these technologies can ultimately augment what’s possible in business and humanity, but not necessarily replace it.”

-Elliot Turner, Director of Alchemy services, IBM Watson

Recently, we partnered with Janet Wagner (@webcodepro), a data journalist, full stack developer and contributor on ProgammableWeb, to put together an eBook with six practical deep learning use cases. We go into more detail on voice search, recommendation engines, image recognition, image tagging, advertising, and pattern recognition, to demonstrate how these technologies integrate into businesses of all shapes and sizes.

These examples will help you grasp the power of deep learning so you can understand what is possible for your business or idea. To keep reading, and to start making sense of your data, get your copy of the eBook.

6 Practical Deep Learning Examples for Today’s Data Driven Business

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