How Deep Learning APIs Can Solve Real-World Data Issues

What is deep learning? In this article we will look at unstructured data challenges and how deep learning APIs can help address these challenges for businesses of all sizes.

Deep learning is a new area of machine learning that works to improve things like computer vision and natural language processing to solve unstructured data challenges. This description is often easier said than understood.

To provide context, in just 1 second, the world’s 3 billion internet users performed more than 50,937 Google searches and viewed more than 109,089 YouTube videos (stats courtesy of Internet Live Stats). These numbers keep growing. That’s not just ‘big data’, that is massive data and the vast majority of it is unstructured.

Businesses dealing with tremendous amounts of data have shifted their focus. Time once dedicated to poring over charts, tables, and spreadsheets is now spent seeking intelligent ways to process that data and automate data analysis. Ultimately, this will allow businesses to connect the dots between what consumers are saying and the actions those businesses are taking.

This shift in the unstructured data approach is due to the widely available technology that is faster, adaptable and more accurate than ever before. Machine learning services, like AlchemyAPI and others, are moving from research labs to enterprise organizations that want to increase agility and better serve customers.

Businesses of all sizes are already using deep learning to transform real-time data analysis, but it can still be hard to explain and to understand. Here are some real-world examples to provide context:

1.Advertising: Deep learning makes it possible for ad networks and publishers to leverage their content to create data-driven predictive advertising, real-time bidding advertising, precisely targeted display advertising, and more.

2.Recommendation Engines: Netflix is a great example of a recommendation engine. Two thirds of the movies that users watch on Netflix are recommended through deep learning.

3.Image Recognition / Face Detection: Image recognition detects faces, identifies demographic information like age and gender, and can even recognize certain celebrity faces.

4.Voice Search / Voice-Activated Assistants: One of the most well known and popular use cases of deep learning APIs is voice-activated intelligent assistants, a feature found on nearly all smartphones.

5.Pattern Recognition: From detecting data anomalies for fraud prevention to discovering subtle patterns that characterize disease profiles for diagnosing complicated diseases, pattern recognition allows companies to monitor and process a multitude of data.

6.Image Tagging / Image Search: If you are on Shutterstock, you’ve seen this work. Image tagging APIs recognize and categorize multiple different types of objects in images, including buildings, nature scenes, and more.

These are just some of the real-world use cases for deep learning. Deep learning is a disruptive technology that is being used by more and more companies to create new business models and to build innovative applications that help solve real problems.

Help others see the value of deep learning applications with this infographic on deep learning real world examples. Click here to get the full infographic: Deep Learning - Real World Examples.

About AlchemyAPI, an IBM Company:

AlchemyAPI, an IBM Company, helps to enable developers and businesses to build smarter applications through a SaaS based platform of cognitive APIs. Available as part of the collection of services on Watson Developer Cloud, AlchemyAPI transforms vast amounts of content, images, and unstructured data into information that can digitally disrupt industries and help drive business decisions. AlchemyAPI leads innovation with natural language understanding and visual recognition, changing the way developers around the world process vast quantities of web-based documents and images.

To learn more, visit alchemyapi.com

For information on IBM Watson, visit ibmwatson.com.

Mackenzie Sedlak

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