### 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.

**Training Slides****Instructor**: Dawei Mu**Date**: September 8, 2021

### 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.

**Training Instructions****Github Repository Notebook****Instructor**: Asad Khan**Date**: September 15, 2021

### 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.

**Training Instructions****GitHub Repository Notebook****Instructor**: Asad khan**Date**: September 22, 2021

### Intro to PyTorch

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

**Training Slides****GitHub Repository Notebook****Instructor**: Joshua Yao-Yu Lin**Date**: September 29, 2021

### 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.

**Training Slides****GitHub Repository Notebook****Instructor**: William Eustis**Date**: October 6, 2021

### 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.

**Training Slides****Instructor**: Dawei Mu**Date**: October 13, 2021

### 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.

**Training Slides****Instructor**: Dawei Mu**Date**; October 20, 2021

### 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.

**DevCloud Example Instructions****GitHub Repository Notebook**: OpenVINO TensorFlow Classification Example**GitHub Repository Notebook:**OpenVINO TensorFlow Object Detection Example**Instructor**: Kumar Vishwesh and Yamini Mimmagadda**Date**: November 3, 2021

### 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.

**Training Slides****arxiv 2110.13330****GitHub Repository Notebook****Instructor**: Avik Roy**Date**: November 10, 2021

### 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.

**Training Slides****GitHub Repository Notebook****Instructor**: Shawn Rosofsky**Date**: November 17, 2021