Math Behind Machine Learning 🤯 | 4 Core Concepts You MUST Know!
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YouTubeDr. Hari Krishan Malhotra
Math Behind Machine Learning 🤯 | 4 Core Concepts You MUST Know!
🤖 Want to master Machine Learning? Start with the mathematics that builds its foundation! In this 60-second video, I explain the 4 major math pillars that power ML: 1️⃣ Linear Algebra – Vectors, matrices, transformations 2️⃣ Calculus – Gradients, rate of change, cost functions 3️⃣ Probability & Statistics – Distributions ...
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Peter Math on Instagram: "Eigenvalue Decomposition is a foundational concept in linear algebra that reveals the internal structure of square matrices. It works by breaking a matrix into its eigenvalues (which scale vectors) and eigenvectors (which define directions that remain unchanged by the matrix transformation). In essence, it tells us how a transformation stretches or compresses space along specific directions—making it a key tool for understanding complex systems. This decomposition is de
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