👉 The math outlined here involves fundamental concepts from linear algebra and calculus, focusing on vector spaces, linear transformations, and optimization techniques. We explore how vectors can be manipulated through matrix operations to represent transformations in space, such as rotations or projections. Key ideas include finding eigenvalues and eigenvectors to understand how transformations affect vector directions, and using gradient descent for optimizing functions by minimizing error. This framework is crucial for solving complex problems in data science, machine learning, and physics, where understanding the behavior of systems under various transformations is essential.
