Archive | Linear Algebra

Gentle Introduction to Eigendecomposition, Eigenvalues, and Eigenvectors for Machine Learning

Gentle Introduction to Eigenvalues and Eigenvectors for Machine Learning

Matrix decompositions are a useful tool for reducing a matrix to their constituent parts in order to simplify a range of more complex operations. Perhaps the most used type of matrix decomposition is the eigendecomposition that decomposes a matrix into eigenvectors and eigenvalues. This decomposition also plays a role in methods used in machine learning, […]

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A Gentle Introduction to Matrix Decompositions for Machine Learning

A Gentle Introduction to Matrix Factorization for Machine Learning

Many complex matrix operations cannot be solved efficiently or with stability using the limited precision of computers. Matrix decompositions are methods that reduce a matrix into constituent parts that make it easier to calculate more complex matrix operations. Matrix decomposition methods, also called matrix factorization methods, are a foundation of linear algebra in computers, even […]

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A Gentle Introduction to Matrix Operations for Machine Learning

A Gentle Introduction to Matrix Operations for Machine Learning

Matrix operations are used in the description of many machine learning algorithms. Some operations can be used directly to solve key equations, whereas others provide useful shorthand or foundation in the description and the use of more complex matrix operations. In this tutorial, you will discover important linear algebra matrix operations used in the description […]

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A Gentle Introduction to N-Dimensional Arrays in Python with NumPy

A Gentle Introduction to NumPy Arrays in Python

Arrays are the main data structure used in machine learning. In Python, arrays from the NumPy library, called N-dimensional arrays or the ndarray, are used as the primary data structure for representing data. In this tutorial, you will discover the N-dimensional array in NumPy for representing numerical and manipulating data in Python. After completing this […]

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