Lesson 1
Vectors
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Mathematics of AI
Exercises
Handwritten work organized by lesson, with each exported page available in reading order.
- Images
- 208
- Historical Reviews
- 8
LessonArtifacts
Lesson 2
Matrices as Linear Transformations
Lesson 3
Matrix Multiplication and Composition
Lesson 4
Determinants
Lesson 5
Inverse Matrices
Lesson 6
Eigenvectors and Eigenvalues
Lesson 7
Change of Basis
Lesson 8
Projection
Lesson 9
Least Squares
Lesson 10
Singular Value Decomposition
Lesson 11
Principal Component Analysis
Lesson 12
Covariance
Lesson 13
Probability as Linear Algebra
Lesson 14
Covariance Geometry
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Lesson 15
Multivariate Gaussian
Lesson 16
Maximum Likelihood Estimation
Lesson 17
Loss Functions
Lesson 18
MAP Estimation
Lesson 19
Bayesian Estimation Introduction
Lesson 20
Bayesian Estimation Update
Lesson 21
Bayesian Predictive Distributions
Lesson 22
Model Complexity
Lesson 23
Regularization
Lesson 24
Early Stopping
Lesson 25
Dropout
Lesson 26
Gradient Descent
Lesson 27
Stochastic Gradient Descent
Lesson 28
Momentum
Lesson 29
Adam
Lesson 30
Learning Rate Schedules
Lesson 31
Hessians and Curvature
Lesson 32
Convexity
Lesson 33
Constrained Optimization
Lesson 34
Partial Derivatives
Lesson 35
Gradients
Lesson 36
Jacobians
Lesson 37
Chain Rule for Computation Graphs
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Lesson 38
Matrix Calculus Notation
Lesson 39
Taylor Series and Local Approximation
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Lesson 40
Perceptrons and Linear Layers
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Lesson 41
Activation Functions
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