Linear Algebra Basics (Vectors, Matrices)

Summary

Vectors and matrices are the data structures that hold every input, weight and embedding in ML and deep learning — nothing in this field works without them.

How to Learn This

1Learn vector/matrix addition, scalar multiplication, and the dot product by hand first.
2Rebuild the same operations in NumPy to connect the math to code.
3Visualize vectors in 2D/3D to build geometric intuition before generalizing to higher dimensions.

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Matrix Operations

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Matrix Operations Eigenvalues & Eigenvectors Calculus Basics (Derivatives, Gradients)Chain Rule Partial Derivatives Probability Fundamentals Probability Distributions Bayes' Theorem Descriptive Statistics Hypothesis Testing Correlation vs Causation Optimization Basics (Gradient Descent)

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