Here you can find all software implementations I have been working with in my spare time. The software is not maintained.
Brain-Tumor-Classifier -Utilizing popular MRI dataset found in Kaggle (see here), this project deals with data exploration, data augmentation and training of classifiers based on pre-trained models such as VGG16, ResNet50, EfficientNet, etc. With the developed GUI one can easily load an MRI image and diagnose with the underlying classifier. Unfortunately, the classifier does not generalize well on images fetched from the internet. Bash scripts that automate the setup of the virtual environment with all the essential dependencies are also provided.
LinearAlgebra -This is an easy-to-install linear algebra library that I developed in C++ during 2017. It consists of two major classes, Vec standing for vector and Mat standing for matrix together
with a set of accompanying functions and operations. This library could very be useful for projects within the field of
signal/image processing and machine learning. My inspiration were three linear algebra libraries Eigen, it++, and Armadillo.
Titanic: ML to the rescue - This is a tutorial I authored on Kaggle in 2021 in
order to practice the Data Science skills I acquired after completing the Data Scientist with Python track on Datacamp.
I demonstrate a thorough methodology of attacking classification problems. I mainly focus on Exploratory Data Analysis (EDA), Data Cleaning,
Feature Engineering (FE), and Machine Learning classifiers, in particular Random Forests.