🚀 Jakub Jaromin
Computer Science student at the Faculty of Applied Mathematics
Silesian University of Technology in Gliwice
📚 About me
I am a computer science student at the Silesian University of Technology in Gliwice. I am particularly interested in fields related to artificial intelligence, machine learning, automation, and programming. In my free time, I develop my skills in programming and AI technologies, and I work on various projects that utilize a wide range of technologies — from no-code platforms (such as n8n) to complex neural network architectures implemented in the PyTorch library using Python. I am also passionate about mathematics and physics, which directly translates into strong analytical and logical thinking skills. Below, I present my most significant projects to date.
🧠 Skills
- Python
- C++
- Linux
- Wolfram
- SQL (PostgreSQL, MySQL, MariaDB),
Vector Database (Weaviate) - Git, Docker
Interested? Contact me:
Email: [email protected]
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📊Radious Angle
Augmentation Method
This project presents a custom data augmentation method implemented in Python, designed to enrich numerical datasets by leveraging statistical properties and density-based sampling. The solution utilizes NumPy and Pandas for efficient data manipulation, Scikit-learn for preprocessing and validation, and Matplotlib and Seaborn for visualizing the augmented distributions and evaluation results.
- Python 3.12
- Scikit-learn
- Pandas
- Numpy
- Matplotlib
- Seaborn
🃏Playing Cards Object
Detection
This project is a real-time playing card recognition system built with deep learning using PyTorch and convolutional neural networks (CNNs). Trained on a structured image dataset, the model delivers high accuracy in classifying card rank, suit, and color. By integrating OpenCV, the system enables live recognition via webcam and provides tools for model evaluation, confusion matrix visualization, and training performance tracking.
- Python 3.12
- PyTorch
- Torchvision
- OpenCV
- Scikit-learn
- Matplotlib
- Seaborn
- Jupyter Notebook
🎸Live Tonation Detection
Live Tonation Classification is a real-time key (tonality) detection system for musical audio, designed for musicians,
music technologists, and audio hackers. It analyzes live audio input (e.g., from a guitar, keyboard, or microphone),
detects the dominant notes, and predicts the musical key (major/minor) on the fly. The project features a modern Tkinter
GUI for instant feedback and is built with modular, readable Python code.
- Python 3.12
- audiodevices
- numpy
- TKinter
💻 SGH x Mastercard Hackathon
Advanced to the finals as one of 8 teams selected from 140 participating teams.
Developed a data-driven solution combining geospatial analysis, a scoring model,
and a prototype web application to identify optimal locations for parcel locker placement.
Our team consisted of 4 members, including myself. From left: Patrycja Prusak, Jakub Jaromin, Michał Lenort, and Wojtek Woźniak.
- Python 3.12
- geopandas
- numpy
- API
- Matplotlib
