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Ever wonder how Facebook’s facial recognition or Snapchat’s filters work? Faces are a fundamental piece of photography, and building applications around them has never been easier with open-source libraries and pre-trained models. In this talk, we’ll help you understand some of the computer vision and machine learning techniques behind these applications. Then, we’ll use this knowledge to develop our own prototypes to tackle tasks such as face detection (e.g. digital cameras), recognition (e.g. Facebook Photos), classification (e.g. identifying emotions), manipulation (e.g. Snapchat filters), and more.
Bio Gabriel
Gabriel is the founder of Scalar Research, a full-service artificial intelligence & data science consulting firm. Scalar helps companies tackle complex business challenges with data-driven solutions leveraging cutting-edge machine learning and advanced analytics. He began his training as a B.S. & M.S. student in computer science at Stanford University, where he received multiple academic distinctions, including the President’s Award for Academic Excellence. He was one of ten students to graduate with honors in computer science in his undergraduate class at Stanford. His thesis investigated quantum deep learning algorithms using NASA’s D-Wave quantum computer, and was selected for a presentation at the AQC 2017 Conference in Tokyo, Japan. During his master’s program, he conducted research at the Stanford Partnership in AI-Assisted Care, a joint lab between the Stanford Computer Science Department (Prof. Fei-Fei Li – Chief Scientist of Cloud AI/ML at Google) and the Stanford School of Medicine (Prof. Arnold Milstein). His research focused on improving clinical care and reducing monitoring costs in hospitals by leveraging machine learning and computer vision, and resulted in a first-author manuscript selected as Top 10 Research Paper at the NIPS Machine Learning for Health 2017 Workshop. Gabriel also has extensive software engineering experience. At Google and Facebook, he worked on backend infrastructure for enterprise tools responsible for billions of dollars in revenue. He’s also created an advertising supply-side platform that handled millions of ad requests per day, built an algorithmic trading platform and quantitative strategies for cryptoasset markets that handled over US$10M in volume, and held positions at startups and investment firms.
