TensorFlow and 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.

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

Next Meeting

Friday, November 15, 2:00-3:30 pm, 138 Lewis Science Library

A Dive in to TensorFlow 2.0
Please join us for this 90-minute workshop, taught at an intermediate level. We will briefly introduce TensorFlow 2.0, then dive in to writing a few flavors of neural networks. Attendees will need a laptop and an internet connection. There is nothing to install in advance, we will use https://colab.research.google.com for examples. We will start with MNIST implemented using a linear model, a neural network, and a deep neural network, followed by a CNN. We will finish with a brief intro to a couple more advanced examples (Deep Dream, Style Transfer, etc).

Speaker: Josh Gordon works on the TensorFlow team at Google, and teaches Applied Deep Learning at Columbia. You can find him online at https://twitter.com/random_forests

Please RSVP for this workshop since space is limited.

Upcoming meetings

TBA, Thursday, January xx, 4:30-5:30 pm, 138 Lewis Science Library

Previous meetings

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


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
Deep Learning Frameworks at Princeton by Jonathan Halverson




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

Kathleen Chen at Princeton