Fundamentals of Deep Learning
This two-part workshop will introduce deep learning techniques and applications.
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. Each session is roughly divided into 3 hours of active learning and 1 hour of extra Q&A. You will work with Python code in a Jupyter notebook environment.
Learning objectives:
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Learn the fundamental techniques and tools required to train a deep learning model
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Gain experience with common deep learning data types and model architectures
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Enhance datasets through data augmentation to improve model accuracy
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Leverage transfer learning between models to achieve efficient results with less data and computation
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Upon successful completion of the assessment, you will receive an NVIDIA certificate. (no longer available)
- Dates & Times:
- 1:00pm - 5:00pm, Monday, September 14, 2026
1:00pm - 5:00pm, Tuesday, September 15, 2026
- Location:
- The Catalyst (Parks 199)
- Campus:
- Parks Library
- Audience:
- Faculty Grad students & postdocs ISU staff Undergrads
- Categories:
- Workshop > The Catalyst
Important
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This workshop is in-person only and will not be recorded.
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This is a two-part course. Make sure you can attend both sessions before you register.
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A laptop is required to participate. Students can borrow a laptop through the library’s Tech Lending program.
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Preferred 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.