Six deep learning applications ready for the enterprise mainstream

Deep learning opens a new level of capabilities within the artificial intelligence realm, but its use has been limited to data scientists. Nowadays, finally, it may be ripe for “democratization,” meaning it is poised to become an accessible set of technologies available to all who need it — with numerous business applications.

library-yale-university-july-2015-photo-by-joe-mckendrick.jpg
Photo: Joe McKendrick

Deep learning, which attempts to mimic the logic of the human brain for analyzing patterns, is starting to see widespread adoption within enterprise AI initiatives. A majority of companies with AI implementations, 53%, plans to incorporate deep learning into their workplaces within the next 24 months, a recent survey of 154 IT and business professionals conducted and published by ITPro Today, InformationWeek and Interop finds.

Deep learning is now driving rapid innovations in AI and influencing massive disruptions across all markets, a new report published by Databricks asserts. “Deep learning models can be trained to perform complicated tasks such as image or speech recognition and determine meaning from these inputs,” the paper’s authors state. “A key advantage is that these models scale well with data and their performance will improve as the size of your data increases.”

The Databricks report defines deep learning as “a specialized and advanced form of machine learning that performs what is considered end-to-end learning. A deep learning algorithm is given massive volumes of data, typically unstructured and disparate, and a task to perform such as classification. The resulting model is then capable of solving complex tasks such as recognizing objects within an image and translating speech in real time.”

The following are applications that are enabled through deep learning:

  • Image classification: “The process of identifying and detecting an object or a feature in a digital image or video,” the report states. In retail, deep learning models “quickly scan and analyze in-store imagery to intuitively determine inventory movement.” 
  • Voice recognition: “The ability to receive and interpret dictation or to understand and carry out spoken commands. Models are able to convert captured voice commands to text and then use natural language processing to understand what is being said and in what context.” In transportation, deep learning “uses voice commands to enable drivers to make phone calls and adjust internal controls – all without taking their hands off the steering wheel.” 
  • Anomaly detection: “Deep learning technique strives to recognize abnormal patterns which don’t match the behaviors expected for a particular system, out of millions of different transactions. These applications can lead to the discovery of an attack on financial networks, fraud detection in insurance filings or credit card purchases, even isolating sensor data in industrial facilities signifying a safety issue.” 
  • Recommendation engines: “Analyze user actions in order to provide recommendations based on user behavior.” 
  • Sentiment analysis: “Leverages deep learning-heavy techniques such as natural language processing, text analysis, and computational linguistics to gain clear insight into customer opinion, understanding of consumer sentiment, and measuring the impact of marketing strategies.”
  • Video analysis: “Process and evaluate vast streams of video footage for a range of tasks including threat detection, which can be used in airport security, banks, and sporting events.”

Popular deep learning frameworks to get started with this technology include TensorFlow, Caffe, MXNet, Keras and PyTorch. 

About the author

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