From Uber using past trips to predict its customers’ future habits to Facebook automatically tagging a picture you upload of your family, data is everywhere these days, and smart companies are using it to inform a better experience for their customers. Could the same be true for your company?
When it comes to making sense of big data, enterprises are heavily invested in machine learning. Simply put, machine learning uses algorithms to find patterns in data fed to it by humans. (There are resources out there for executives who want a high-level overview of this approach.)
Typically, machine learning deals with data that is relatively simple. This low-dimensional data, whether structured or unstructured, can be analyzed in light of a handful of factors. But eventually, companies started amassing a lot of highly complex data, things like images. That meant it was time for more sophisticated analytics tools. Enter deep learning.
“Wait,” you may be thinking. “I’ve been using those terms interchangeably. Aren’t they the same thing?” That is a pretty widespread assumption, but deep learning is exactly as it sounds, deeper.
Traditional machine learning requires humans to provide context for data — something called feature engineering — so a machine can make better predictions. Deep learning uses a layered approach to make better decisions by constantly curating the data it is fed. It simplifies feature engineering in many ways, putting more of the work on machines, and ever more complex, self-learning models. Essentially, deep learning can assess and categorize data like our five human senses, and then make correlations more akin to the human mind.
How can deep learning change your business?
So how do you know if deep learning is right for your business? For starters, you need a lot of data for it to work. It’s a powerful tool, but you need a really complex problem to use it effectively. The amount of data produced every day sits around 2.5 exabytes [an exabyte is one billion gigabytes], and businesses now equate parsing through all this data as a solution to their business problems. And with deep learning, they now have smart machines to parse through their most complex, multidimensional data to gain new insights.
Deep learning is great for video, speech or images. Traditional machine learning models can’t make heads-or-tails of complex images, for example. Yet deep learning with computer vision, can, relatively easily, teach itself the difference between cats and dogs. Vision and image detection are great deep learning applications. With them, businesses can track sentiment from pictures on Instagram. Or image recognition can be built into apps, so users that want to re-purchase an item can simply capture it on the camera — a current feature of Amazon’s app — to place the item in their shopping cart.
- by Mo Patel, practice director of AI and deep learning at Teradata
Read the source article at informationweek.com.