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NVIDIA Deep Learning Institute: Fundamentals of Deep Learning

When

Thursday, August 20, 2026, 9am – 5:30pm MDT

Campus Wide Event

Yes

Description

NVIDIA and the University of Utah are pleased to invite you to attend an upcoming hands-on technical training workshop:

Workshop Name: Fundamentals of Deep Learning
Date: Thursday, August 20, 2026
Time: 9:00 a.m. to 5:30 p.m. MDT
Location: 303 Chipeta Way (Research Park), Salt Lake City, UT; details, including the room number, will be provided by email to registrants in advance of the workshop (registration is required)

This training is offered exclusively to verifiable academic students, staff, and researchers. Please use your institutional email address when registering. Registrants using personal email addresses will not receive further details about the event.

About This Workshop:
Businesses worldwide are using artificial intelligence to solve their greatest challenges. Healthcare professionals use AI to enable more accurate, faster diagnoses in patients. Retail businesses use it to offer personalized customer shopping experiences. Automakers use it to make personal vehicles, shared mobility, and delivery services safer and more efficient. Deep learning is a powerful AI approach that uses multi-layered artificial neural networks to deliver state-of-the-art accuracy in tasks such as object detection, speech recognition, and language translation. Using deep learning, computers can learn and recognize patterns from data that are considered too complex or subtle for expert-written software.

Workshop Agenda:

Introduction (30 minutes)
The Mechanics of Deep Learning (3 hours)
Break (1 hour)
Pre-trained Models and Large Language Models (1.5 hours)
Break (15 minutes)
Final Project: Object Classification (1 hour)
Final Review (30 minutes)

Learning Objectives:

Learn the fundamental techniques and tools required to train a deep learning model
Gain experience with common deep learning data types and model architectures
Enhance datasets through data augmentation to improve model accuracy
Leverage transfer learning between models to achieve efficient results with less data and computation
Build confidence to take on your own project with a modern deep learning framework

Topics Covered:

PyTorch
Convolutional Neural Networks (CNNS)
Data Augmentation
Transfer Learning
Natural Language Processing

Prerequisites:
An understanding of fundamental programming concepts in Python 3, such as functions, loops, dictionaries, and arrays; familiarity with Pandas data structures; and an understanding of how to compute a regression line.

Please register on this calendar event using your institutional email address. If you have questions about this workshop, please contact helpdesk@chpc.utah.edu.

This workshop is brought to you by NVIDIA and the University of Utah.

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