Thursday, Jan 25th (12pm-1pm MT)

 

 

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.

 

Thursday, Feb 8th (12pm-1pm MT)

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 to train and evaluate a neural network model using Keras.

 

Thursday, Feb 22nd (12pm-1pm MT) 

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.  

 

Thursday, Mar 7th (12pm-1pm MT)

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.

 

Thursday, Mar 28th (12pm-1pm MT) 

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.

 

Thursday, Apr 18th (12pm-1pm MT) 

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.

 

Thursday, May 2nd (12pm-1pm MT) 

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.