Trainings + Workshops

NVIDIA and Mark III are hosting an AI/Machine Learning Educational Series for Oregon State University and its greater community. The series features industry experts in Machine Learning who will dive into current trends around AI/ML via tutorials and hands-on rapid labs designed around practical AI education, delivered remotely via Jupyter Notebooks. These sessions are virtual and will be recorded.

   The next series of new trainings and workshops will begin spring term.


Past Recorded Trainings

Intro to Machine Learning and AI:  The Basics, A Tutorial, and Lab

In this session, we’ll cover the basics around what Machine Learning is, look at the different ML techniques and methods, examine what a typical ML project lifecycle looks like, and discuss some of the most commonly used example algorithms.

This session will also include a 20-minute rapid mini-workshop via Jupyter Notebook where we'll use an example dataset from Kaggle and take it through the steps of training and evaluating a model to make predictions using ML.  Examples of labs include classifying tumors as malignant or benign using ML, predictive maintenance (anomaly detection), and pricing prediction.

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Intro to Deep Learning: An Introduction to Neural Networks

In this session, we'll cover the basics around what Deep Learning is, look at how it fits within the AI/ML universe, dive into neural networks (including CNNs, LSTMs, and GANs), and walk through a typical Deep Learning project lifecycle.  

We'll cap the session with a 20-minute rapid mini-workshop via Jupyter Notebook where we'll use an example dataset (CIFAR-10) to train and evaluate a neural network model using Keras.

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Introduction to Datasets

In this session, we'll cover what datasets for Machine Learning and Deep Learning projects look like and how to find them.

This will include highlighting some of the most popular datasets in the community today as well as good sources to download these datasets from.

Some brief tips and tricks for cleaning up datasets will be covered and we'll conclude the session with a mini-workshop and lab showing how to import and interact with datasets in a Jupyter Notebook for a public health use case.

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Intro to Computer Vision and Image Analytics

In this session, we'll cover the basics around what computer vision is, how it works (classification, object detection, segmentation), some of the popular frameworks and models used today, and what some of the practical applications might be in research and industry.  We'll also walk through what a typical Computer Vision project lifecycle might look like.

We'll cap the session with a 20-minute rapid mini-workshop via Jupyter Notebook where we'll use code examples to build a CNN image classifier as well as using pre-trained model libraries for object detection and image segmentation.

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Getting Started with Containers and AI

This session will cover the ML/DL ecosystem of container-powered technologies and the best ways to get started and accelerate your journey in building, training, deploying, and scaling your models.  We'll touch on NVIDIA NGC, Docker, Kubernetes, Singularity, and other ways to get started and get going quickly.

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Getting Started with Containers and the software stack around AI + How to get started working with OSU HPC Services

This session will cover the ML/DL ecosystem of container-powered technologies and the best ways to get started and accelerate your journey in building, training, deploying, and scaling your models, with the NVIDIA ecosystem software stack.  We'll touch on NVIDIA NGC, Docker, Kubernetes, Singularity, and other ways to get started and get going quickly.

In addition, this session will cover an overview on how to get started working with OSU HPC Services, if you need an HPC/AI cluster to train, deploy, and inference with larger models.

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Introduction to Large Language Models

In this session, we'll overview the landscape around LLMs and Generative AI and look into a few of the most popular frameworks for training and using LLM models, including Mosaic MPT, Falcon, and Nemo.  This session will also include a Jupyter Notebook lab that will take attendees through the process of finetuning a simple LLM model for a sample disease diagnosis use case.

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Intro to Omniverse + Digital Twins

In this session, we'll cover the basics around NVIDIA's Omniverse platform for 3D Design Collaboration and Simulation and the ecosystem of building Digital Twins.  We'll touch on not only how to set up and rollout an Omniverse environment, but also how to integrate frameworks, like Modulus (physics simulations) and Isaac (robotics) into Omniverse to visualize your models and research.  Whether your work is focused on Engineering, Climate, Biomed, Robotics, Architecture, Natural Sciences, Computer Science, Business, or Data Science, we'll have something for you in this session.

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Intro to Machine Learning and AI: What it is and why do we need it?

In this session, we’ll cover the basics around what Machine Learning is, look at the different ML techniques and methods, examine what a typical ML project lifecycle looks like, and discuss some of the most commonly used example algorithms.

This session will also include a 20-minute rapid mini-workshop via Jupyter Notebook where we'll use an example dataset from Kaggle and take it through the steps of training and evaluating a model to make predictions using ML.  Specifically the use case will cover using quantitative data to predict benign and malignant tumors, based on a large dataset.

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Intro to Deep Learning: An Introduction to Neural Networks

In this session, we'll cover the basics around what Deep Learning is, look at how it fits within the AI/ML universe, dive into neural networks (including CNNs, LSTMs, and GANs), and walk through a typical Deep Learning project lifecycle.  

We'll cap the session with a 20-minute rapid mini-workshop via Jupyter Notebook where we'll use an example dataset (CIFAR-10) to train and evaluate a neural network model using Keras.

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