TensorFlow & PyTorch User Group

The deep learning research community at Princeton comprises over 10 academic departments and more than 200 researchers. The TensorFlow and PyTorch User Group was created to serve as a campus-wide platform for researchers to connect with one another to discuss their work and the use of the tools. In addition to monthly presentations by graduate students and postdoctoral researchers, the group hosts external speakers from such companies as Google, NVIDIA and Intel. All members of the PU research community are welcome. Subscribe to the mailing list.

    Getting Started with TensorFlow at Princeton
    Getting Started with PyTorch at Princeton

The group is sponsored by the Princeton Institute for Computational Science and Engineering (PICSciE) and the Center for Statistics and Machine Learning (CSML).


Announcement

Request for Proposals: Faculty Awards to Support Machine Learning Courses, Diversity, and Inclusion

Google AI and the TensorFlow team have a new funding opportunity open to universities. If you’re a faculty member interested in teaching machine learning courses, and/or leading or contributing to diversity initiatives, please read on to learn more. We have parallel goals for these awards, and you may apply for funding with one or both in mind.


Upcoming meeting

TBD


Previous meetings

FebRuary 2020

Foundations of Deep Learning with PyTorch, Prof. Alf Canziani, NYU

NOVEMber 2019

A Dive in to TensorFlow 2.0 by Josh Gordon, Google

October 2019

Leveraging Intel Software Libraries for Accelerated AI Research by Jonathan Halverson
Continual adaptation for efficient machine communication by Robert D. Hawkins
Accelerating automated modeling and design with stochastic optimization and neural networks by Alex Beatson

September 2019

JAX: Accelerated machine learning research via composable function transformations in Python by Peter Hawkins, Google AI

 

July 2019

Selene: A PyTorch-based Deep Learning Library for Sequence Data by Kathleen Chen
Big data of big tissues: deep neural networks to accelerate analysis of collective cell behaviors in large populations by Julienne LaChance
GPU Computing with R and Keras by Danny Simpson
Announcements and TensorFlow 2 (beta) by Jonathan Halverson

 

June 2019

Opportunities and challenges in self-driving cars at NVIDIA by Timur Rvachov (slides not available)
Training deep convolutional neural networks by Michael Churchill, PPPL
Deep Learning Frameworks at Princeton by Jonathan Halverson

 

 

Contact

For more information please contact Jonathan Halverson (halverson@princeton.edu)

 
Kathleen Chen at Princeton