This section very briefly describes some of the mathematical concepts used in this book.

Many algorithms used in machine learning such as minimization of a convex loss function, principal component analysis, or least squares regression invariably involves manipulation and transformation of matrices. There are many good books on the subject, from the inexpensive [A:2] to the sophisticated [A:3].

The QR decomposition (or the QR factorization) is the decomposition of a matrix *A* into a product of an orthogonal matrix *Q* and upper triangular matrix *R*. So, *A=QR* and *Q ^{T}*

The decomposition is unique if *A* is a real, square, and invertible matrix. In the case of a rectangle matrix *A*, *m by n* with *m > n*, the ...

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