NVIDIA and Mark III would like to host an AI/Machine Learning Educational Series for Oregon State University and its greater community in Spring 2024. These sessions would be 100% virtual and will 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. We hope you can join us!
AI/ML Education Series:
Upcoming Sessions:
Tuesday, April 30th (11am-12pm PT)
Introduction to Datasets
Speaker: Data Scientist, Mark III
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 7th (11am-12pm PT)
Introduction to Large Language Models
Speaker: Data Scientist, Mark III
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 14th (11am-12pm PT)
Getting Started with Containers and the software stack around AI + How to get started working with OSU HPC Services
Speakers: Architects, Mark III & OSU
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 21st (11am-12pm PT)
Intro to Omniverse & Digital Twins
Speaker: Innovation Dev, Mark III
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 28th (11am-12pm PT)
Intro to Isaac Sim and AI in Robotics
Speaker: Solutions Architect, NVIDIA
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.
Replays (Past Sessions):
Tuesday, April 16th (11am-12pm PT)
Intro to Machine Learning and AI: The Basics, A Tutorial, and Lab
Speaker: Data Scientist, Mark III
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 23rd (11am-12pm PT)
Intro to Deep Learning: An Introduction to Neural Networks
Speaker: Data Scientist, Mark III
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.