Math for AI

19 lessons View examples

Mathematics for AI/ML/DL - Overview

00_Overview.md

01. Vectors and Matrices

01_Vectors_and_Matrices.md

02. Matrix Decompositions

02_Matrix_Decompositions.md

03. Matrix Calculus

03_Matrix_Calculus.md

04. Norms and Distance Metrics

04_Norms_and_Distances.md

05. Multivariate Calculus

05_Multivariate_Calculus.md

06. Optimization Fundamentals

06_Optimization_Fundamentals.md

07. Gradient Descent Theory

07_Gradient_Descent_Theory.md

08. Probability for Machine Learning

08_Probability_for_ML.md

09. Maximum Likelihood and MAP

09_Maximum_Likelihood_and_MAP.md

10. Information Theory

10_Information_Theory.md

11. Advanced Probability Distributions

11_Probability_Distributions_Advanced.md

12. Sampling and Monte Carlo Methods

12_Sampling_and_Monte_Carlo.md

13. Linear Algebra for Deep Learning

13_Linear_Algebra_for_Deep_Learning.md

14. Convexity and Duality

14_Convexity_and_Duality.md

15. Graph Theory and Spectral Methods

15_Graph_Theory_and_Spectral_Methods.md

16. Manifold Learning and Representation Learning

16_Manifold_and_Representation_Learning.md

17. Mathematics of Attention and Transformers

17_Math_of_Attention_and_Transformers.md

18. Mathematics of Generative Models

18_Math_of_Generative_Models.md