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.