This project is a result of an in-depth exploration into the integration of pre-calculus concepts, with a strong emphasis on matrices, within the domain of neural networks. As a part of a research project, the primary goal was to implement a neural network in Rust, leveraging matrices for various operations, and gaining a profound understanding of their fundamental role in machine learning.
The project heavily relies on matrix operations, a fundamental aspect of linear algebra, within the context of neural networks. Understanding matrices is crucial for comprehending essential operations like forward and backward propagation, underlining the pivotal role of pre-calculus knowledge.
This project was conducted as part of a quick research aimed at looking deeper into the practical implementation of matrices in neural networks. It sought to broaden the understanding of matrix manipulation, activation functions, and backpropagation within the neural network context. The research's primary objective was to gain more knowledge and skills within the topic, establishing a seamless connection between newfound pre-calculus principles and the intricacies of modern machine learning.