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Phase 01

Math Foundations

The intuition behind every AI algorithm, through code, not textbooks.

Lessons (22)

  1. 01Linear Algebra Intuition
  2. 02Vectors, Matrices & Operations
  3. 03Matrix Transformations
  4. 04Calculus for Machine Learning
  5. 05Chain Rule & Automatic Differentiation
  6. 06Probability and Distributions
  7. 07Bayes' Theorem
  8. 08Optimization
  9. 09Information Theory
  10. 10Dimensionality Reduction
  11. 11Singular Value Decomposition
  12. 12Tensor Operations
  13. 13Numerical Stability
  14. 14Norms and Distances
  15. 15Statistics for Machine Learning
  16. 16Sampling Methods
  17. 17Linear Systems
  18. 18Convex Optimization
  19. 19Complex Numbers for AI
  20. 20The Fourier Transform
  21. 21Graph Theory for Machine Learning
  22. 22Stochastic Processes