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AI From Scratch/Phase 01/Lesson 02/~60 minutes

Vectors, Matrices & Operations

BuildPythonJulia

Every neural network is just matrix multiplication with extra steps.

Back to phaseMath Foundations Next lessonMatrix Transformations

Phase 01

Math Foundations

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

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Phase 01

Math Foundations

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

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Resources

3Blue1Brown: Essence of Linear AlgebraReading NumPy documentation on broadcastingReading Stanford CS229 Linear Algebra ReviewReading Matrix OperationsPrompt matrices.jlCode matrices.pyCode