Linear Algebra used in Machine Learning

Linear algebra is a branch of mathematics that is widely used throughout science and engineering. Yet because linear algebra is a form of continuous rather than discrete mathematics, many computer scientists have little experience with it.

A good understanding of linear algebra is essential for understanding and working with many machine learning algorithms, especially deep learning algorithms. We therefore precede our introduction to deep learning with a focused presentation of the key linear algebra prerequisites.

In machine learning, the majority of data is most often represented as vectors, matrices or tensors. Therefore, the machine learning heavily relies on the linear algebra.

  • A vector is a 1D array. For instance, a point in space can be defined as a vector of three coordinates (x, y, z). Usually, it is defined in such a way that it has both the magnitude and the direction.
  • A matrix is a two-dimensional array of numbers, that has a fixed number of rows and columns. It contains a number at the intersection of each row and each column. A matrix is usually denoted by square brackets [].
  • A tensor is a generalization of vectors and matrices. For instance, a tensor of dimension one is a vector. In addition, we can also have a tensor of two dimensions which is a matrix. Then, we can have a three-dimensional tensor such as the image with RGB colors. This continues to expand to four-dimensional tensors and so on.

Furthermore, many operations can be applied on them like addition, subtraction, multiplication, etc.

Applications of Linear Algebra

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