Computer vision and deep OCR in the enterprise: 3 use cases
Who is this presentation for?
- IT professionals in the healthcare and medical industry
The use of deep learning in the field of computer vision is a relatively new concept, with the first major breakthrough coming in 2012 during the ImageNet competition. That was the first time a deep learning network outperformed traditional computer vision techniques, and it won by a landslide. The ImageNet challenge was to classify images of objects (i.e., balloons, cats, etc.) across ~20 thousand categories. Even today, years after the first use of deep learning in computer vision, the kind of problems that are commonly studied is the classification of objects that are easily differentiable visually (i.e., person versus tree). Dave Ferrell explores three examples of nontraditional techniques pushing the boundaries of computer vision in industries today, including identifying “unseen” objects.
- A basic understanding of artificial intelligence, algorithms, and deep learning
What you'll learn
- Discover three examples of nontraditional techniques in computer vision
Dave Ferrell is the managing director of Dynam.AI, a company focused on helping organizations of any size, stage, or industry maximize the potential of artificial intelligence, where he leads a team bringing groundbreaking AI-driven solutions to enterprises around the world. A 25-year industry veteran, Dave has founded and operated companies in web hosting, web design, web app development, back-office systems, and systems integration. In addition to sourcing and managing engineering talent from around the world, Ferrell has led the development of over 600 projects for some of the technology industry’s most innovative companies.
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