I’m a Computer Science student.
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I’m currently working on my degree, machine learning projects. My focus areas include machine learning, audio signal processing, and computer vision. I believe in results, whether it’s reducing error rates in a neural network or improving my personal best time on a 5K run.
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Music Genre Classification: Think you can tell the difference between rock and pop? Well, I taught a machine to do that. Using CNNs, and MLP, this project classifies music genres with the precision of a metronome. Inspired by Valerio Velardo - The Sound of AI.
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Lie Detection Using Facial Recognition: Ever wondered if someone’s lying to you? This project won’t help with that, but it does analyze court trial videos using InceptionV3 and LSTM to detect deception. It’s all about the subtle facial cues—like when someone says they "only skipped leg day once." Dataset courtesy of the University of Michigan's Real-Life Deception Detection project.
- Machine Learning: If it can be modeled, I’ve probably done it. And if not, I’m working on it.
- Audio Signal Processing: Why listen to music when you can teach a machine to understand it?
- Computer Vision: Sure, I can see faces, but I’ve trained machines to see them better.
- Sports Analytics: Data doesn’t lie, unlike some players on the field. I analyze and use numbers to predict and enhance performance.
- Training both my models and myself (life’s about balance, right?).
- Languages: Python, C, C++, C#, Java, JavaScript
- Frameworks: TensorFlow, Keras, OpenCV, Librosa
- Tech: Machine Learning, Deep Learning, Transfer Learning, Computer Vision, Audio Processing
- Tools: Git, Docker, Jupyter Notebooks, and maybe a pair of boxing gloves