Tensors: Motivations and Introduction

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Covers tensors, Lorentz transformations, and electrodynamics in relativistic notation.

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Covers the multivariate normal distribution, properties, and sampling methods.

Covers the basics of linear algebra, including solving equations and understanding systems of linear equations graphically.

Provides an overview of symmetric matrices, quadratic forms, and their applications in linear algebra and analysis.

Covers the transformation of vectors and tensors in quantum physics.

Explores linear applications in R² and matrix representation, including basis, operations, and geometric interpretation of transformations.

Covers tensors, loss functions, autograd, and convolutional layers in deep learning.

Covers the norm of a matrix, operator, singular values, and unitary matrices in linear algebra.