Developed and fine-tuned CNN-based classifiers using pre-trained models like EfficientNetB3, ResNet50, and MobileNetV2 to classify 80 types of military aircraft from images. Created a custom data generator and applied image preprocessing to ensure compatibility. Achieved up to 95% training accuracy and 97% test accuracy with EfficientNetB3 as a base model. Visualizations of how the different models perform are available on GitHub.

Developed CNN and RNN models to classify ECG heartbeats as normal or myocardial infarction using time-series data. Implemented a data generator for batching and applied one-hot encoding to ensure compatibility with softmax. Achieved 88+% test accuracy through hyperparameter tuning and architecture experiments. Experiment visualizations are avaialble on GitHub.

Developed a feed-forwad neural network to predict thyroid cancer recurrence using numerical and categorical features. Applied encoding and normalization for optimal input processing. Achieved 95+% test accuracy through hyperparameter tuning. Experiment visualizations are avaialble on GitHub.

My animated and responsive portfolio website, built with NextJS, React and Tailwind CSS written in TypeScript. The site allows users to view a little about me, my work experience and previous projects that I've worked on. It also features a contact form equipped with reCAPTCHA and form validation to reduce spam.

Reinforcement learning agent utilizing a CNN built with PyTorch. The agent is trained on random racetrack configurations with the goal of reaching the finish line as fast as possible while staying on the track and minimizing traction loss.