Mark III Systems and NVIDIA will be hosting an AI/Machine Learning Education Series for MUSC, Clemson University, the MUSC-Clemson AI Hub and their greater communities for Fall 2022. These sessions are 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, delivered remotely via Jupyter Notebooks. We hope you can join us!
AI/ML Education Series Replays (Fall 2022):
Wednesday, Sept 14th (4pm-5pm 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, in this case, an ML model that predicts benign or malignant tumors, based on quantitative data.
Wednesday, Sept 28th (4pm-5pm ET)
Introduction 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.
Wednesday, Oct 12th (4pm-5pm ET)
Introduction to Datasets
Speaker: Data Scientist, Mark III Systems
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.
Wednesday, Oct 26th (4pm-5pm ET)
Introduction to Computer Vision
Speaker: Data Scientist, Mark III Systems
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.
Wednesday, Nov 9th (4pm-5pm ET)
Getting Started with Containers and AI
Speakers: Solutions Architects, NVIDIA & NetApp
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. We'll touch on NVIDIA NGC, Docker, Kubernetes, Singularity, NetApp's DataOps Toolkit & AI Control Plane, and other ways to get started and get going quickly.
AI/ML Education Series Replays (Spring 2022):
Wednesday, Feb 2nd (4pm-5pm 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.
Wednesday, Feb 16th (4pm-5pm ET)
Introduction 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.
Wednesday, March 2nd (4pm-5pm ET)
Introduction to Datasets
Speaker: Data Scientist, Mark III Systems
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.
Wednesday, March 16th (4pm-5pm ET)
Introduction to Computer Vision
Speaker: Data Scientist, Mark III Systems
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.
Wednesday, March 30th (4pm-5pm ET)
Getting Started with Containers and AI
Speakers: Solutions Architects, NVIDIA & NetApp
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. We'll touch on NVIDIA NGC, Docker, Kubernetes, Singularity, NetApp's DataOps Toolkit & AI Control Plane, and other ways to get started and get going quickly.