Tuesday, Oct 29th (11am-12pm PT)

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.

Tuesday, Nov 12th (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.

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.