Python

Have you ever wondered if an AI can tell your favorite anime characters apart? Developed as part of the IBM AI Engineering certification course, this project explores exactly that. Using PyTorch, I built and trained a Convolutional Neural Network (CNN) to perform Anime Face Classification. To test the waters, we’re starting with a head-to-head visual showdown: teaching the model to accurately distinguish between the characters anastasia and takao. Authors Jedrzej Golaszewski Title Anime Face Classification Status Completed Date Dec 28, 2025 Tech stack PyTorch, Python Links GitHub - Kaggle Dataset The original dataset was used in the ‘AniWho’ study (Read Paper). We use the Kaggle distribution of this dataset. ...

An AI-Powered Career Concierge & Automated Resume Tailoring System. Developed as part of the 5-Day AI Agents Intensive Course with Google. ...

This project focuses on building an automated image processing pipeline to process, align, and morph consecutive slices of tissue samples from Whole Slide Images (WSI). By correcting structural deformations introduced during physical tissue sectioning and scanning, the pipeline is able to construct smooth intermediate representations (morphs) between sequential tissue layers. This approach helps in visualizing gradual morphological changes in biological tissues in 3D-like continuity. Authors Jedrzej Golaszewski Title MiMorph Status Completed Date Sep 2, 2025 Tech stack OpenSlide, scikit-image, OpenCV, SimpleITK, PyWavelets, Optuna, NumPy Links GitHub Pipeline Architecture The pipeline is split into four distinct stages, moving from raw slide extraction to the final morphed outputs. ...