PyData Trójmiasto #21 [GPNT]

We are happy to announce our first meetup in 2023!
This time we are welcoming everyone to join for two great talks from Jan Glinko (DAC.digital) and Franciszek Górski (GUT, Gradient PG).

When:
23.02.2023 (Thursday), at 18:00.

Where:
Gdański Park Naukowo-Technologiczny (https://gpnt.pl/)
Room 002, Building B.

Agenda:
18:00 - 18:05 meeting boarding
18:05 - 18:10 A few words about PyData Trójmiasto
18:10 - 18:50 Jan Glinko - Meta-learning for fast Neural Network fine-tuning
18:50 - 19:20 Franciszek Górski - Cancer classification in luqiud biopsy
19:20 - Pizza & networking

About talks

Talk#1
Meta-learning for fast Neural Network fine-tuning
(PyTorch, Higher, custom data loader, training script preparation, batch normalization issues)

The speed of learning, whether it is pattern recognition or the acquisition of new skills, is a characteristic of human intelligence. Meta-learning is a field of machine learning that attempts to replicate how humans learn new tasks. This type of fast and flexible learning, benefiting from previous experience, differs from the standard approach to training deep neural networks. In summary, our goal is no longer a model that generalizes well, but becomes one that adapts well.

Key points:
● When is it worth using meta-learning?
● Types of meta-learning
● Using Model-Agnostic Meta-Learning to solve a regression problem

About speaker:
Jan Glinko works as a Machine Learning Researcher at DAC.digital in Gdansk, Poland. He graduated from the Faculty of Electronics, Telecommunications, and Informatics at the Gdansk University of Technology. He is interested in applying synthetic datasets for learning deep neural networks and in learning algorithms to reduce the amount of data required for effective network training.

Talk#2
Machine learning algorithms for cancer classification of liquid biopsy data

Early cancer detecetion is a key step in a successfull treatment. Recent development of a liquid biopsy method, which is a simple noninvasive method and discovering a Tumor Educated Platelets mechanism boosted the studies on using machine learning algorithms for cancer classification task.
In my presentation I would speak about my works during collaboration with Center for Biostatistics and Bioinformatics from Gumed with such liquid biopsy data, designed for cancer classification. During these works we fit and validated various machine learning algorithms like neural networks, kNNs or gradient boosted trees. We trained them for binary and multiclass classification problems. I would speak about our results and various attemps for processing of our data like dimensionality reduction, attemps to creating a graphs or using autoencoders for classification task.

About speaker:
Franciszek is a Deep Learning researcher from ETI Faculty of Gdańsk University of Technology whose interests focus on using neural networks and machine learning algorithms in various problems including biometric verification, cancer classification, object detection and more. He is working in Multimedia Systems Department as a researcher and collaborate with Center for Biostatistics and Bioinformatics from Gumed. In addition, Franciszek is also a co-chairman of Gradient Science Club, which integrates the Deep Learning community on GUT.

Both talks will be presented in english.