Deep Learning / 02
Math for Deep Learning
Work through the linear algebra, calculus, probability, and optimization mathematics used in deep learning.
This is a knowledge index. Published titles open their articles; the remaining entries outline planned coverage.
Linear Algebra
- 02.01
Scalars
Planned guide: Scalars. Focus on intuition, assumptions, and a worked example.
- 02.02
Vectors
Planned guide: Vectors. Focus on intuition, assumptions, and a worked example.
- 02.03
Matrices
Planned guide: Matrices. Focus on intuition, assumptions, and a worked example.
- 02.04
Tensors
Planned guide: Tensors. Focus on intuition, assumptions, and a worked example.
- 02.05
Vector Operations
Planned guide: Vector Operations. Focus on intuition, assumptions, and a worked example.
- 02.06
Matrix Multiplication
Planned guide: Matrix Multiplication. Focus on intuition, assumptions, and a worked example.
- 02.07
Dot Product
Planned guide: Dot Product. Focus on intuition, assumptions, and a worked example.
- 02.08
Transpose
Planned guide: Transpose. Focus on intuition, assumptions, and a worked example.
- 02.09
Identity Matrix
Planned guide: Identity Matrix. Focus on intuition, assumptions, and a worked example.
- 02.10
Inverse Matrix
Planned guide: Inverse Matrix. Focus on intuition, assumptions, and a worked example.
- 02.11
Eigenvalues
Planned guide: Eigenvalues. Focus on intuition, assumptions, and a worked example.
- 02.12
Eigenvectors
Planned guide: Eigenvectors. Focus on intuition, assumptions, and a worked example.
Calculus
- 02.13
Functions
Planned guide: Functions. Focus on intuition, assumptions, and a worked example.
- 02.14
Derivatives
Planned guide: Derivatives. Focus on intuition, assumptions, and a worked example.
- 02.15
Partial Derivatives
Planned guide: Partial Derivatives. Focus on intuition, assumptions, and a worked example.
- 02.16
Gradients
Planned guide: Gradients. Focus on intuition, assumptions, and a worked example.
- 02.17
Chain Rule
Planned guide: Chain Rule. Focus on intuition, assumptions, and a worked example.
- 02.18
Jacobian
Planned guide: Jacobian. Focus on intuition, assumptions, and a worked example.
- 02.19
Hessian
Planned guide: Hessian. Focus on intuition, assumptions, and a worked example.
Probability
- 02.20
Probability Fundamentals
Planned guide: Probability Fundamentals. Focus on intuition, assumptions, and a worked example.
- 02.21
Random Variables
Planned guide: Random Variables. Focus on intuition, assumptions, and a worked example.
- 02.22
Probability Distributions
Planned guide: Probability Distributions. Focus on intuition, assumptions, and a worked example.
- 02.23
Expectation
Planned guide: Expectation. Focus on intuition, assumptions, and a worked example.
- 02.24
Variance
Planned guide: Variance. Focus on intuition, assumptions, and a worked example.
- 02.25
Conditional Probability
Planned guide: Conditional Probability. Focus on intuition, assumptions, and a worked example.
- 02.26
Bayes' Theorem
Planned guide: Bayes' Theorem. Focus on intuition, assumptions, and a worked example.
Optimization Mathematics
- 02.27
Objective Functions
Planned guide: Objective Functions. Focus on intuition, assumptions, and a worked example.
- 02.28
Convex vs Non-Convex Functions
Planned guide: Convex vs Non-Convex Functions. Focus on intuition, assumptions, and a worked example.
- 02.29
Local vs Global Minima
Planned guide: Local vs Global Minima. Focus on intuition, assumptions, and a worked example.
- 02.30
Gradient-Based Optimization
Planned guide: Gradient-Based Optimization. Focus on intuition, assumptions, and a worked example.