Linear Algebra in Go
The same mathematics as the crash course, written in Go against gonum instead of explained in Python. Vectors and basic operations, matrix fundamentals, solving linear systems, eigenvalue problems, SVD and decompositions, statistics and data analysis, a regression library built from parts, PCA, the matrix operations underneath a neural network, and the performance work at the end.
This is implementation rather than instruction. Each part builds working code and covers where the obvious version breaks down numerically. If the mathematics itself is new, read the crash course first and come back.
Ten parts, starting with vectors.
1
Linear Algebra in Go: Vectors and Basic Operations
Linear Algebra in Go Part 1
2020-10-10
2Linear Algebra in Go: Matrix Fundamentals
Linear Algebra in Go Part 2
2020-12-15
3Linear Algebra in Go: Solving Linear Systems
Linear Algebra in Go Part 3
2021-02-20
4Linear Algebra in Go: Eigenvalue Problems
Linear Algebra in Go Part 4
2021-04-25
5Linear Algebra in Go: SVD and Decompositions
Linear Algebra in Go Part 5
2021-06-30
6Linear Algebra in Go: Statistics and Data Analysis
Linear Algebra in Go Part 6
2021-09-05
7Linear Algebra in Go: Building a Regression Library
Linear Algebra in Go Part 7
2021-11-10
8Linear Algebra in Go: PCA Implementation
Linear Algebra in Go Part 8
2022-01-15
9Linear Algebra in Go: Neural Network Foundations
Linear Algebra in Go Part 9
2022-03-20
10Linear Algebra in Go: High-Performance Computing
Linear Algebra in Go Part 10
2022-05-25