Wednesday, Feb 7th (5pm-6pm ET)

Introduction to Datasets & Data Wrangling

Speaker: Data Scientist, Mark III

In this session, we'll delve into the world of datasets for ML and Deep Learning, explore popular public datasets, and learn tips for normalizing and cleaning up datasets.

This session will conclude with a mini-workshop and practical lab showing how to import and interact with open cardiology datasets in a Jupyter Notebook.  

Wednesday, Feb 21st (5pm-6pm ET)

Introduction to Machine Learning and AI:  What is it and why we 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.

Wednesday, Mar 6th (5pm-6pm ET) 

Introduction to Deep Learning and 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 to train and evaluate a neural network model using Keras.

Wednesday, Mar 27th (5pm-6pm ET)

MONAI for AI and Medical Imaging

Speaker: NVIDIA MONAI Leader, NVIDIA & Data Scientist, Mark III

MONAI is an open source project and set of collaborative frameworks built on top of PyTorch designed to accelerate research and clinical collaboration around Medical Imaging. 

Frameworks include MONAI Label, Core, and Deploy, and enables image labeling and learning, data integrations and extensions, training via PyTorch, and the deployment and operation of models built using MONAI.  We'll cap this session with a lab that will utilize MONAI to label and train on a dataset of sample medical images.

Wednesday, Apr 17th (5pm-6pm ET)

Introduction 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 also include a Jupyter Notebook lab that will take attendees through the process of training and finetuning a simple LLM model.

Wednesday, Apr 24th (5pm-6pm ET) 

Deep Dive Tutorial into LLMs (NeMo)

Speaker: Data Scientist, Mark III

In this tutorial and short working session, we'll take a look at the NeMo framework for LLMs and show examples of a couple popular models that can be trained and deployed via NeMo.  This session will be half practical tutorial and half live interactive demo.

Wednesday, May 15th (5pm-6pm ET) 

Introduction to Computer Vision

Speaker: Data Scientist, Mark III

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 pretrained model libraries for object detection and image segmentation.