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Fundamentals of Deep Learning by Jon Krohn

Deep Learning algorithms have become influential across a broad range of the statistical domain, including classification, prediction, and feature generation. Recently popularized by a standout performance in the 2012 iteration of the ImageNet visual recognition competition, Deep Neural Networks have since facilitated countless everyday applications, including Tesla's Autopilot, Siri's voice recognition, and Google Inbox's suggested replies. Via analogy to biological neurons and human vision, this talk is an introduction to artificial neural networks that incorporates interactive demos and example code from popular open-source Deep Learning libraries. About Speaker: Jon Krohn is the Chief Data Scientist at untapt, a machine learning-driven recruitment start-up based in New York. Previously, he deployed algorithms to automate predictions in the fields of brain imaging, genomics, finance, and digital advertising. Jon obtained his Doctorate in Neuroscience at Oxford University as a Wellcome Trust Scholar and has published in high-impact journals like Neurology, Nature Genetics, and Neural Information Processing Systems (NIPS). Every third week, he hosts a Deep Learning Study Group at untapt's Midtown HQ.