NVIDIA and Mark III would like to host an AI/Machine Learning Educational Series for Oregon State University and its greater community in Fall 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, Dec 3rd (11am-12pm PT)
Intro to Large Language Models (LLMs)
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 include a Jupyter Notebook lab that will take attendees through the process of training and finetuning a simple LLM model.
Tuesday, Dec 10th (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.
Replays (Past Sessions):
Monday, Nov 4th (11am-12pm PT)
Introduction to Machine Learning and AI: What is it and why do we need it?
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. Specifically the use case will cover using quantitative data to predict benign and malignant tumors, based on a large dataset.
Tuesday, Nov 26th (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.