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).
Foundations of Deep Learning with PyTorch, Prof. Alf Canziani, NYU
A Dive in to TensorFlow 2.0 by Josh Gordon, Google
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
JAX: Accelerated machine learning research via composable function transformations in Python by Peter Hawkins, Google AI
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
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
For more information please contact Jonathan Halverson ([email protected])