## Linear Algebra in Python [Lesson 5]. Inverse Matrix and Matrix Rank

The fifth lesson is devoted to finding the inverse matrix, its properties, and determining the rank of a matrix. Inverse matrix The inverse matrix A -1 of a matrix A is...

The fifth lesson is devoted to finding the inverse matrix, its properties, and determining the rank of a matrix. Inverse matrix The inverse matrix A -1 of a matrix A is...

September 13, 2024

The fourth lesson from the series “Linear Algebra in Python ” is devoted to the concept of the determinant of a matrix and its properties. Determinant of a...

September 13, 2024

The topic of the third lesson: operations on matrices . Within its framework, the following questions will be considered: multiplication of a matrix by a number, addition and...

September 13, 2024

In this lesson we will look at the operation “ matrix transpose ” and how it is performed in Python . We will also look at the properties of this...

September 13, 2024

Matrices In mathematics, a matrix is an object written in the form of a rectangular table, the elements of which are numbers (they can be both real...

September 13, 2024

Creating vectors and matrices Vectors and matrices are the main objects that you have to operate with in machine learning. Numpy provides quite a few handy functions that...

September 13, 2024

Let’s look at a powerful tool for accessing ndarray data in the Numpy library – boolean arrays. With their help, you can get subsamples and modify data in the original array. Creating boolean arrays...

September 13, 2024

The Numpy library provides functions for calculating simple statistics: mean, median, standard deviation, etc. This lesson is devoted to the issue of using these functions. Introduction Import the Numpy library...

September 13, 2024

The article discusses various ways to obtain elements from arrays of the numpy.ndarray type of the numpy library . If you are reading this article, you probably know what the numpy library...

September 13, 2024

Very often, large volumes of data that are prepared for subsequent analysis have gaps. In order to be able to use machine learning algorithms that build...

September 13, 2024

Two approaches to accessing data in pandas When working with Series and DataFrame structures from the pandas library , there are typically two main ways to get element values. The first method is...

September 13, 2024

Introduction The pandas library provides two structures: Series and DataFrame for fast and convenient work with data (in fact, there are three of them, there is one more structure – Panel ,...

September 13, 2024

This is the first lesson in a series dedicated to the p andas library. This series will be part of a large group of training materials, the topic of...

September 13, 2024

What is a higher order function? Throughout this series of articles, we have repeatedly encountered High Order Functions ( HOF ) , this is clearly written about in the article...

September 13, 2024

A bit of theory Normal and applicative order of computation To begin with, let’s turn to the theory of programming languages, namely to the topic of...

September 13, 2024

Calculations without using assignment Let’s start with math. Look at the expression below: c =a2+b2——√ Using this formula, you can calculate the hypotenuse of a right triangle,...

September 13, 2024

Let’s consider two approaches that define the view of what programs and data are. The first is from the position of von Neumann architecture and the...

September 13, 2024

Introduction If we have functions that are first-class objects , we can build levels of abstraction that allow us to build computations that have increasingly general properties. This...

September 13, 2024

First, let’s turn to Wikipedia for definitions of the above terms: Abstraction (Latin: abstractio — distraction) — theoretical generalization as a result of abstraction. Abstraction — distraction in the process of...

September 13, 2024