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math-for-ml

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A beginner-friendly collection of Jupyter notebooks covering the essentials of linear algebra with clear NumPy code and visualizations. Topics include vectors, basis, transformations, dot product, determinants, eigenvalues/eigenvectors, matrix inverse, rank, and cross product—ideal for students and self-learners.

  • Updated Jun 4, 2026
  • Jupyter Notebook

A NumPy-only Multilayer Perceptron (MLP) built from first principles to demystify neural networks, backpropagation, and optimization algorithms.

  • Updated Jan 27, 2026
  • Jupyter Notebook

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