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We are very happy to welcome you after the summer break to the 26th edition of PyData Trójmiasto prepared together with Nike!
Great talks, networking, pizza, and cool gifts included!;)
When: 20th September at 17:30
Where: the event is hosted by Nike at Nike PTC Office, Olivia Business Centre!
[Registration - obligatory to enter] Please register officially HERE
The capacity is limited. Be sure to save your seat!
Agenda:
17:30-17:45 - Gathering and Welcome
17:45-18:15 - Nike Office Tour
18:15-18:20 - Opening
18:20-19:10 - Fine-tuning text-to-image generative AI models
19:10-20:00 - Introducing Optuna – next-generation hyperparameter
20:00-21:00 - Networking & Pizza
About Nike and the talks:
NIKE, Inc. is a technology company. From developing products, managing big data, and providing leading-edge engineering and systems support, our teams at NIKE Global Technology reimagine the future at the confluence of tech and sport.
Fine-tuning text-to-image generative AI models by Maciej Mackiewicz
The presentation will cover how to fine-tune a Stable Diffusion text-to-image model on custom data. Besides regular fine tuning, we will go over Low Rank Adaptation method that can speed up the training time and greatly reduce the size of fine-tuned artefact.
About Maciej:
Senior Machine Learning Engineer at Nike with seven years of experience in software development, machine learning engineering and MLOps. Alumni of Big Data studies at Warsaw School of Economics. Most passionate about working with text data.
Introducing Optuna – next-generation hyperparameter optimization by Mateusz Serocki
The presentation will be about the Optuna package that allows you to tune model hyperparameters in optimal time. We will compare that solution to other most common approaches. During that part, we will also see how Optuna can visualize results and we will talk about the interpretation of those visualization. As a final step, you will learn how to use Optuna for tunning neural network structure and optimization algorithm.
About Mateusz:
I’m Senior Machine Learning Engineer at NIKE. Throughout my carrier I was working on various topics, mostly demand forecast, classification, and regression problems. I’m fully focusing on a deep understanding of math that stands behind the machine learning algorithms.
In addition, be welcome to join our new discord group!
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