*********************************
There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
*********************************
Atlanta, GA | Posted: August 27, 2020
Georgia Tech’s Partnership for Advanced Computing (PACE) hosted Introduction to Machine Learning and Deep Learning hands-on workshops throughout the summer.
Summary Sentence:
Georgia Tech’s Partnership for Advanced Computing (PACE) hosted Introduction to Machine Learning and Deep Learning hands-on workshops throughout the summer.
Full Summary:
Georgia Tech’s Partnership for Advanced Computing (PACE) hosted Introduction to Machine Learning and Deep Learning hands-on workshops throughout the summer.
Dr. Nuyun "Nellie" Zhang
Savannah QuinnGeorgia Tech’s Partnership for Advanced Computing (PACE) hosted Introduction to Machine Learning and Deep Learning hands-on workshops throughout the summer. Dr. Nuyun "Nellie" Zhang, research scientist and team lead at PACE, instructed workshops garnering more than 400 registrations and 300 remote participants. Zhang was joined by PACE undergraduate student employee, Savannah Quinn, who served as a teaching assistant.
During these three-hour workshops students were introduced to the basic concepts of machine learning – supervised learning and unsupervised learning, including neural networks and deep learning – before engaging in interactive coding exercises with practical programming skills, general workflows and optimization best practices followed by question and answer sessions. Participants - primarily comprised of Ph.D and master's students - as well as some undergraduates, staff, faculty, and post doctorates, were given the chance to work with Scikit-Learn, Tensorflow2.2, and Keras machine learning libraries through open-source web applications like Jupyter Notebook and Google Colab with PACE-ICE clusters provided as the computational resource. Outside of the Bluejeans sessions, participants were encouraged to utilize resources provided on Canvas for continued learning or independent study.
Through the workshops, many participants felt empowered to apply the concepts they learned to tackle machine learning projects in their respective disciplines ranging from Mechanical Engineering to Language and Design. In an anonymous survey distributed post-workshop, participants were incredibly pleased with the breadth of topics covered as well as the pace of the lectures and opportunity for hands on learning. Zhang and PACE look forward to hosting many more rewarding workshops this fall.
