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Rotation matrix

A rotation matrix is a square matrix representing a linear transformation that rotates vectors while preserving their lengths and angles.

How 2 Manning books explain it

Math and Architectures of Deep Learning

A matrix that represents a geometric rotation in this section, with orthogonal rotation matrices being length-preserving and invertible by transposition.

How it is used
Uses rotations to connect geometric transformations with matrix properties and machine-learning tools.
How deep it goes
Develops formulas, geometric properties, PyTorch checks, eigen-axes, and hyperellipse alignment without extending into later applications.
Examples
Demonstrates planar and axis-specific rotations, sequential 3D rotations, PyTorch assertions, and a 45-degree hyperellipse axis.
What you can do after
Apply it via PyTorch to construct rotations, transform vectors, verify orthogonality, reverse angles, and find hyperellipse axes.

Read how Math and Architectures of Deep Learning explains rotation matrix →

Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI

A rotation matrix is a square orthogonal matrix that preserves vector lengths and angles under transformation.

How it is used
Transforms input features before machine-learning models to test model robustness and rotational behavior.
How deep it goes
Defines preservation and reversibility, then explains model consequences without deriving matrix construction.
Examples
Uses rotated tabular features to compare deep architectures with gradient-boosted decision trees.
What you can do after
Evaluate model behavior by applying reversible rotations and interpreting their effects on tree-based and deep models.

Read how Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI explains rotation matrix →

Related concepts

These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.