Deep Learning: Data Scientists Add Deep Learning to their Toolkits

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Deep Learning is quickly becoming a key component in the tool bag for Data Scientists.  Deep Learning is a kind of Machine Learning that is being applied to applications like fraud detection, prodcut demand prediction, quality assurance, and predictive and prescriptive maintenance.

Alexander Linden, research vice president at Gartner, said that “deep learning is here to stay and expands ML by allowing intermediate representations of the data. It ultimately solves complex, data-rich business problems. Deep learning can, for example, give promising results when interpreting medical images in order to diagnose cancer early. It can also help improve the sight of visually impaired people, control self-driving vehicles, or recognize and understand a specific person’s speech.”

Gartner predicts that over the next year that 80 percent of data scientists will adopt the use of machine learning.

Gartner said that “focus on data as the fuel for machine learning by adjusting your data management and information governance for machine learning. Data is your unique competitive differentiator. Although the choice of machine-learning algorithms is fairly limited, data sources are abundant and a good long-term investment.”

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