Vector and matrix arithmetic applies operations across ordered collections of numbers. Neural networks depend on this computation, so performance matters when processing many values at once. section 7.3
Vector and matrix arithmetic
Vector and matrix arithmetic is numerical computation on ordered collections and rectangular arrays of numbers.
How Classic Computer Science Problems in Swift: Essential techniques for practicing programmers covers it
- How deep it goes
- Explains vector operations and optimization choices, but does not develop matrix algorithms or formal linear algebra.
- Examples
- Demonstrates dot products, subtraction, elementwise multiplication, summation, and random weight generation.
- What it assumes
- Assumes familiarity with Swift arrays and numerical values, while treating vector arithmetic as operations over lists.
- What you can do after
- Apply it via Accelerate functions to implement common vector computations in Swift.
- How it connects
- Connects neural networks with SIMD, GPUs, CPUs, BLAS, and the Accelerate framework.
Related concepts
- vector arithmetichas part · Vector arithmetic is the application of operations such as addition, subtraction, scalar multiplication, and weighted combinations to numerical vectors.
This explainer was generated by Manning's knowledge graph from the text of Classic Computer Science Problems in Swift: Essential techniques for practicing programmers. The section links point to the parts of the book it draws on.