Trainings + Workshops
Spring Trainings
NVIDIA and Mark III are hosting an AI/Machine Learning Educational Series for Oregon State University and its greater community. The spring trainings feature 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.
Tuesday, April 16, 11am to noon — recording now available
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.
Tuesday, April 23, 11am to noon — recording now available
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.
Tuesday, April 30, 11am to noon
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.
Tuesday, May 7, 11am to noon
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.
Tuesday, May 14, 11am to noon
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.
Tuesday, May 21, 11am to noon
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.
Tuesday, May 28, 11am to noon
Intro to Isaac Sim and AI in Robotics
In this session, we'll focus on NVIDIA's Isaac Sim platform, which is an extensible robotics simulator that gives researchers and practitioners a faster, better way to design, test, and train AI-based robots. Isaac Sim is powered by NVIDIA Omniverse and able to deliver scalable, photorealistic, and physically accurate virtual environments for building high-fidelity simulations.
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.
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.
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.
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.
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.