Neural Networks Demystified
Details
Join us April 10 for a 45 minute talk, followed by open networking and snacks!
Location: Goergen 101, University of Rochester.
The goal of this talk is to cover exactly how neural network training is implemented in a way that's accessible for those with little to no machine learning or calculus experience.
The broad scope of prerequisite knowledge required to fully understand modern neural networks can be daunting to say the least, including calculus, statistics, linear algebra, and more. This talk aims to build a conceptual understanding, with a special focus on backpropagation. With this foundation in place, additional tools from linear algebra and statistics will be more easily understood.
About the presenter: Evan Raw is a software engineer specializing in Linux system programming, observability, and performance engineering. He previously developed automated provisioning, configuration management, network monitoring, and SCADA telemetry systems for utility companies. More recently, he has worked on high-performance machine vision systems, including algorithm design, network programming, and embedded control-system firmware.
Neural Networks Demystified