Six deep learning applications ready for the enterprise mainstream

“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.”

A deep learning algorithm is given massive volumes of data, typically unstructured and disparate, and a task to perform such as classification.

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.

Another example, 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.”