Tesla's self-driving sedans, Facebook's Newsfeed, Apple's voice recognition and Google Inbox's suggested replies are all powered by algorithms from the burgeoning field of Deep Learning.
Despite their tremendous real-world utility, these Artificial Intelligence techniques – developed by machine-learning researchers working at the intersection of statistics and computer science – are not tremendously complex.
Via analogies to biological neurons and the primate visual system, Dr. Krohn will describe how layers of artificial neurons facilitate Deep Learning. The talk will survey contemporary applications and empower audience members to build their own Deep Learning algorithms.
Speaker: Jon Krohn
Jon Krohn is the Chief Data Scientist at untapt, a machine learning-driven recruitment platform based in New York. He has previously deployed algorithms to automate predictions in the fields of biology, finance, and digital advertising.
Before obtaining his doctorate in Neuroscience at Oxford University as a Wellcome Trust Scholar, Jon studied and conducted research at Wilfrid Laurier University.
He has published in influential peer-reviewed journals like NIPS, Neurology, and Nature Genetics.