Eigenvectors explained (Maths Behind AI)

What are Eigenvectors and why are they important in Artificial Intelligence? In this video, we visually explain the concept of **eigenvectors**, one of the most important ideas in Linear Algebra and a key part of the mathematics behind AI and Machine Learning. Eigenvectors represent directions that remain unchanged during a transformation, while eigenvalues indicate how much the data is scaled along those directions. These concepts are widely used in machine learning techniques such as dimensionality reduction and data transformation. Through simple visual animations, this video demonstrates how eigenvectors work and why they are essential for understanding how AI models process and simplify complex data. In this video you will learn: • What eigenvectors are • What eigenvalues represent • How transformations affect vectors • Why eigenvectors remain in the same direction • How eigenvectors are used in Machine Learning and AI Eigenvectors play a crucial role in techniques like Principal Component Analysis (PCA), which helps reduce data dimensions while preserving important patterns. This channel explains AI concepts using clear visual explanations to make complex ideas simple and intuitive. Subscribe for more videos on: Artificial Intelligence, Machine Learning, Deep Learning, Neural Networks, and the mathematics behind AI. #artificialintelligence #machinelearning #linearalgebra #eigenvectors #aiexplained #agenticai #generativeai