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Getting Started with Qwickly – Fox Online & Digital Learning

https://foxonline.temple.edu/guides/canvas/getting-started-with-qwickly/

Students should open their Canvas app, navigate to the correct course, and select “Attendance.” They can enter the PIN, if required, or select “Check In.”

iGEM - Institute for Genomics and Evolutionary Medicine | Temple University

https://igem.temple.edu/products/software/mega

iGEM researchers are pursuing a variety of interdisciplinary research and discovery projects

Academic Resource Center | Temple University Undergraduate Studies

https://undergradstudies.temple.edu/arc

The academic home for undeclared students and students in transition between majors. Our advisors are dedicated to helping you choose a major that's right for you.

Oh, Hell! Spring 2021 CIS5603 Final Project Card Playing AI Bot Jack ...

https://cis.temple.edu/~pwang/5603-AI/Project/2021S/Amend_Zach/Amend_Zach_FinalPresentation.pdf

Card Playing AI Bot Jack Amend and Cameron Zach CIS5603 Final Project Spring 2021

the 27th annual be your own boss bowl® - Fox School of Business

https://www.fox.temple.edu/faculty-research/institutes-centers/innovation-entrepreneurship-institute/competitions/byobb

Submission Requirements The Be Your Own Boss Bowl® (BYOBB®) Competition started in 1997 at Temple University as the “Business Plan Competition,” and has since evolved into one of the nation’s most lucrative pitch competitions for aspiring entrepreneurs, with a total prize package including cash prizes and in-kind services awards worth over $100,000. The BYOBB® is open to all Temple ...

PSFL: Parallel-Sequential Federated Learning with Convergence Guarantees

https://cis.temple.edu/~jiewu/research/publications/Publication_files/INFOCOM2024_PSFL%20Parallel-Sequential%20Federated%20Learning%20with%20Convergence%20Guarantees.pdf

Abstract—Federated Learning (FL) is a novel distributed learning paradigm which can coordinate multiple clients to jointly train a machine learning model by using their local data samples. Existing FL works can be roughly divided into two categories according to the modes of model training: Parallel FL (PFL) and Sequential FL (SFL). PFL can speed up each round of model training time through ...