Foundations for Machine Learning

Currently Playing: Foundations for ML | Linear Algebra | Transformation with non-square matrices 2D to 3D [Lecture 7]

"Mathematical Foundations for Machine Learning: Exploring Non-Square Matrices and Transformations" Linear algebra concepts like determinants and transformations are foundational in machine learning, but they can often feel abstract. In my latest lecture on Vizuara’s YouTube channel, "Foundations for Machine Learning | Non-Square Matrices and Transformations," we explore these ideas from an intuitive angle. In my latest lecture on Vizuara’s YouTube channel titled "Foundations for Machine Learning | Determinants and Linear Transformations," we approach determinants from a new angle. Rather than seeing them as mere numbers, we delve into how they represent the stretching, squishing, or even collapsing of areas in 2D space. This lecture breaks down: 1) How matrices represent transformations: From rotations and scaling to complex transformations that alter dimensionality, we explore what’s happening under the hood. 2) Non-square matrices and dimension changes: We look at how a 3x2 matrix can map a 2D space into a plane in 3D space, and how a 2x3 matrix can compress a 3D space into a 2D plane. 3) Applications in machine learning: We discuss how non-square matrices reshape data in various ways to process inputs. This lecture is part of a larger 45-hour course I have developed over the past 4 months. This course, Foundations for Machine Learning, spans around 65 lectures, each designed to build an intuitive understanding of mathematical and programing foundation for machine learning. The focus is on simplifying complex concepts through geometric and logical intuition, providing a clear path for those interested in machine learning without requiring a deep mathematical background. Explore this new lecture on and see how this essential concept can transform the way we understand space and transformations in machine learning: https://youtu.be/UA1-e0D9ybY