This repository is an appendix for a study I wrote for a linear algebra class at Lafayette College. You can find the paper here.
This paper investigates the mathematical foundation and application of linear algebra in artificial neural networks (ANNs), a widely used model in machine learning (ML). We begin by explaining the mathematical building blocks of ML algorithms, highlighting how their structure relies on linear algebra. The discussion then transitions from theory to practice by implementing an ANN to solve the XOR problem and running a prediction through manual calculations.
I study computer science and French at Lafayette College, where I focus my research on machine learning interpretability and multilingual NLP. My work explores how artificial systems understand language, and how we can make those systems more transparent, inclusive, and useful across disciplines. You can read more about me, my work, and my teaching on my website.