Training

A Shallow Introduction to Deep Learning with PyTorch - Evann Courdier (Idiap, EPFL)

A Modern Guide to Hyperparameter Optimization - Richard Liaw (AnyScale, UC Berkeley)

Deep Generative Models - Aditya Grover (Stanford University)

Reproducibility in Deep Learning - Koustuv Sinha (McGill University)

Uncertainty and Out-of-Distribution Robustness in Deep Learning - Balaji Lakshminarayanan, Dustin Tran and Jasper Snoek (Google Brain)

How to Evaluate Efficient Deep Neural Network Approaches - Vivienne Sze (MIT)

Symmetry and Equivariance in Neural Networks - Tess Smidt (Berkeley Lab)

Distributed Large Batch Training - Swetha Mandava (NVIDIA)

Attention & Language - Rami Al-Rfou (Google Research)

Hidden Physics Models - Maziar Raissi (University of Colorado Boulder)

Qualitative Choices in Representations for Molecules, Materials, and Surfaces - Zachary Ulissi (Carnegie Mellon University)

Introduction to Machine Learning - Brenda Ng

Overview of NERSC Deep Learning Stack - Wahid Bhimij

Introduction to Neural Networks 1 - Mustafa Mustafa

Introduction to Neural Networks 2 - Mustafa Mustafa

Building Neural Nets using Keras - Steven Farrell

TensorFlow 2.0 Ecosystem - Josh Gordon

Getting Your Models to Scale: Practicalities in deep learning - Joel Hestness

Deep Learning Reproducibility - Jessica Forde

Fairness and Ethics in Machine Learning - Emily Denton

Sequential Models - Luke de Oliveira

Generative Models - Emily Denton

Deep Learning for Science at NERSC - Prabhat

GANs for HEP - Ben Nachman

Deep Learning for Molecular Engineering - Jennifer Wei

Deep Learning for Quantum Chemistry - Justin Smith

Hyperparameter Optimization - Ben Albrecht

Featurewise Transformations - Vincent Dumoulin

Interpretability - Been Kim

Object Detection and Image Segmentation - Alexander Kirillov

Scaling Neural Networks Training - Thorsten Kurth

Geometric Deep Learning - Or Litany

Scaling NNs Training (Hands on) - Steven Farrell