Deep Learning User Group

The Deep Learning User Group currently operates through a small number of in-person meetings per year. The meetings provide an excellent opportunity to find out what others are doing, exchange tips, learn from lightning talks, and discuss issues with members of Research Computing. Anyone interested in machine learning is welcome to attend. Subscribe to the PICSciE/RC mailing list to receive updates.

Be aware of the getting started guides by Princeton Research Computing for PyTorch, JAX and TensorFlow.

To volunteer to help with planning and putting on future events, send e-mail to [email protected].

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


Upcoming meeting

Deep Learning and Pizza
Fall 2024

Join your fellow deep learning enthusiasts to discuss all matters of deep learning, get tips from members of Research Computing, and learn about what is going on at Princeton. We will provide updates on PyTorch, JAX and TensorFlow.


Previous meetings

Machine Learning and Pizza
March 2, 2023 at 12:00-1:00 PM

JAX: A Machine-Learning Research Library by Peter Hawkins, Google AI
November 7, 2022 at 4:30-5:30 PM.

TensorFlow and Pizza
October 25, 2022 at 12:00-1:00 PM

PyTorch and Pizza
September 28, 2022 at 12:00-1:00 PM

Foundations of Deep Learning with PyTorch, Prof. Alf Canziani, NYU
February 2020

A Dive in to TensorFlow 2.0 by Josh Gordon, Google
November 2019

Leveraging Intel Software Libraries for Accelerated AI Research by Jonathan Halverson
Continual adaptation for efficient machine communication by Robert Hawkins
Accelerating automated modeling and design with stochastic optimization and neural networks by Alex Beatson
October 2019

JAX: Accelerated machine learning research via composable function transformations in Python by Peter Hawkins, Google AI
September 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
July 2019

Opportunities and challenges in self-driving cars at NVIDIA by Timur Rvachov
Training deep convolutional neural networks by Michael Churchill, PPPL
Deep Learning Frameworks at Princeton by Jonathan Halverson
June 2019



For more information, please send an email to [email protected].