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What is Deep Learning?

By   /  October 16, 2017  /  No Comments

deep learningDeep Learning is a type of Machine Learning, using neural networks to grasp complex patterns. This type of technology allows Artificial Intelligence systems to perform human like tasks, “such as recognizing real-life objects or understanding speech.” While the operation of these brain-inspired networks remains inscrutable, their interconnected layers algorithms give machines the ability to be trained and carry out specific tasks. The algorithms that drive this process account for a greater number of and more abstract variables.

Other Definitions of Deep Learning Include:

  • “A specific type of Machine Learning where the learning happens in successive layers ˗ each layer adding knowledge of the previous layer.” (Paramita Ghosh, DATAVERSITY®)
  • “Technology that imitates the actions of a human brain, allowing software to train and explore an environment on its own, with minimal human intervention.” (Keith D Foote, DATAVERSITY®)
  • “A form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts.” (Goodfellow, et. al. Deep Learning, MIT Press)
  • “The cutting-edge of the cutting-edge technology focusing more narrowly on a subset of Machine Learning tools and techniques and applies them to solving” problems requiring thought. (Bernard Marr, Forbes)
  • “A branch of Artificial Intelligence inspired by the structure of the human brain…. [that gives] machines the ability to intuit the physical world.” (Aditya Singh, Harvard Business Review)
  • Software attempting “to mimic the activity in layers of neurons in the neocortex, the wrinkly 80 percent of the brain where thinking occurs. The software learns, in a very real sense, to recognize patterns in digital representations of sounds, images, and other data.” (Robert D Hof, MIT Technology Review)

Businesses use Deep Learning to:

 

Photo Credit: TierneyMJ/Shutterstock.com

About the author

Michelle Knight enjoys putting her information specialist background to use by writing technical articles on enhancing Data Quality, lending to useful information. Michelle has written articles on W3C validator for SiteProNews, SEO competitive analysis for the SLA (Special Libraries Association), Search Engine alternatives to Google, for the Business Information Alert, and Introductions on the Semantic Web, HTML 5, and Agile, Seabourne INC LLC, through AboutUs.com. She has worked as a software tester, a researcher, and a librarian. She has over five years of experience, contracting as a quality assurance engineer at a variety of organizations including Intel, Cigna, and Umpqua Bank. During that time Michelle used HTML, XML, and SQL to verify software behavior through databases Michelle graduated, from Simmons College, with a Masters in Library and Information with an Outstanding Information Science Student Award from the ASIST (The American Society for Information Science and Technology) and has a Bachelor of Arts in Psychology from Smith College. Michelle has a talent for digging into data, a natural eye for detail, and an abounding curiosity about finding and using data effectively.

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