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2021 Fall Series

Getting Started with HAL

Overview: This tutorial will introduce students to HAL system, how to interact with it through Open OnDemand interface, including Jupiter notebook, and through command line interface via SSH.  The tutorial will cover the Anaconda environment, batch job submission, data transfer, as well as basics of machine learning. 

Hands-On Deep Learning for Computer Vision

Overview: This tutorial will introduce how machine learning can be accomplished with neural networks and will go over various examples from simple dense networks to convolutional network architectures using TensorFlow on HAL system.

Intro to TensorFlow

Overview: This tutorial will introduce basics of TensorFlow necessary to build a neural network, train it and evaluate the accuracy of the model.

Intro to PyTorch

Overview: This tutorial will teach how to build and train neural networks in PyTorch on HAL.

Data Loaders

Overview: The main objective of this tutorial is to show how to use data loaders provided with PyTorch and how to develop application-specific data loaders.

Machine LEarning with h20 on hal

Overview: This tutorial will cover basic machine learning techniques, such as Linear Regression, Decision Tree, Support Vector Machine, Naive Bayes, K-Nearest Neighbors, K-Means, and Random Forest, using on HAL system.

distributed deep learning on hal

Overview: This tutorial will explain how to use multiple GPUs for accelerating deep learning on HAL using frameworks such as TensorFlow or PyTorch.

INTRO TO NATURAL LANGUAGE PROCESSING (NLP) WITH PYTORCH

Overview: This tutorial will introduce neural network architectures for processing natural language texts. We will cover Recurrent Neural Networks (RNNs) and Generative Neural Networks (GNNs), attention mechanisms, and how to build text classification models.

  • Instructor: Volodymyr Kindratenko
  • Date: October 27, 2021

OPENVINO™ TOOLKIT INTEGRATION WITH TENSORFLOW: HANDS-ON PRACTICE INTEL DEVCLOUD

Overview: This tutorial will introduce Tensorflow integration available with the OpenVINO™ toolkit and hands-on practice in the Intel® DevCloud explaining the minimal code changes needed with Tensorflow to gain greater inference performance with OpenVINO.  Suggested to pre-register for access to the Intel® DevCloud here. Access is free.

ROBUST PHYSICS INFORMED NEURAL NETWORKS

Overview: Physics Informed Neural Networks (PINNs) have recently been found to be effective PDE solvers. This talk will focus on how traditional PINN architectures along with physics-inspired regularizers fail to retrieve the intended solution when training data is noisy and how this problem can be solved using Gaussian Process based smoothing techniques.

PHYSICS INFORMED DEEP LEARNING

Overview; This tutorial will explore how to incorporate physics into deep learning models with various examples ranging from using physics informed neural networks (PINNs) for forward and inverse problems to employing physics informed DeepONets for a hybrid data and physics approach to problems.