References

The problem this chapter solves is:

The course uses small Rust examples. These references point to the larger Rust, ML, and category-theory treatments behind those examples.

Use each reference with the same three questions:

Rust syntax:
which source file in this course uses the idea?

ML concept:
which model or training behavior does the reference explain?

Category theory concept:
which object, morphism, composition, or law does it deepen?

Rust

• The Rust Programming Language: Packages, Crates, and Modules explains how Rust packages are organized into library and binary crates. Use it with src/lib.rs, src/bin/category_ml.rs, and the examples/ files.
• The Rust Programming Language: Recoverable Errors with Result explains the error pattern behind CtResult<T> and constructors such as Distribution::new.
• The rustdoc book: How to write documentation explains the documentation comments used above public types and methods.
• Rust API Guidelines Checklist is a practical review checklist for naming, documentation, type conversions, and error design.

Category Theory

• Seven Sketches in Compositionality: An Invitation to Applied Category Theory is the larger applied-category-theory text behind the companion chapter. Use it with src/sketches.rs.
• Seven Sketches in Compositionality PDF is the direct paper file for offline reading and page-by-page study.

Machine Learning

• Dive into Deep Learning: Softmax Regression explains multiclass classification, logits, softmax, and cross entropy. Use it with src/ml.rs.
• Dive into Deep Learning: Softmax Regression Implementation from Scratch shows the implementation path behind this course’s smaller Rust version.
• Stanford CS231n: Linear Classification explains linear classifiers, scores, losses, and the softmax classifier from a widely used university course.

Transformers

• Attention Is All You Need on arXiv is the original Transformer paper.
• Attention Is All You Need on the NeurIPS proceedings site is the archival conference listing.
• Dive into Deep Learning: Attention Mechanisms and Transformers is a practical bridge from softmax and vector operations to attention and Transformer blocks.