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Linear Algebra With Python

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Linear Algebra With Python

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Author : Makoto Tsukada
language : en
Publisher: Springer Nature
Release Date : 2023-12-06

Linear Algebra With Python written by Makoto Tsukada and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-06 with Mathematics categories.

This textbook is for those who want to learn linear algebra from the basics. After a brief mathematical introduction, it provides the standard curriculum of linear algebra based on an abstract linear space. It covers, among other aspects: linear mappings and their matrix representations, basis, and dimension; matrix invariants, inner products, and norms; eigenvalues and eigenvectors; and Jordan normal forms. Detailed and self-contained proofs as well as descriptions are given for all theorems, formulas, and algorithms. A unified overview of linear structures is presented by developing linear algebra from the perspective of functional analysis. Advanced topics such as function space are taken up, along with Fourier analysis, the Perron–Frobenius theorem, linear differential equations, the state transition matrix and the generalized inverse matrix, singular value decomposition, tensor products, and linear regression models. These all provide a bridge to more specialized theories based on linear algebra in mathematics, physics, engineering, economics, and social sciences. Python is used throughout the book to explain linear algebra. Learning with Python interactively, readers will naturally become accustomed to Python coding. By using Python’s libraries NumPy, Matplotlib, VPython, and SymPy, readers can easily perform large-scale matrix calculations, visualization of calculation results, and symbolic computations. All the codes in this book can be executed on both Windows and macOS and also on Raspberry Pi.

Linear Algebra Coding With Python

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Author : Hyun-Seok Son
language : en
Publisher: Hyun-Seok Son
Release Date : 2020-08-11

Linear Algebra Coding With Python written by Hyun-Seok Son and has been published by Hyun-Seok Son this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-11 with Art categories.

Python is one of the most popular languages for data analysis and prediction. What's more, tensorflow and torch, useful tools of recent deep learning, are fully implemented by Python. The basic form of data in these languages is an array, created by Python's important package numpy. In particular, arrays are the basis of data science because they have structures of vectors and matrices that give the meaning of direction and magnitude to each value in the data set. The matrix structure allows transformation to a simple form without losing the basic characteristics of a vast data set. These transformations are useful for efficient processing of data and for finding implicit characteristics. Linear Algebra, a field that provides a basic theory of vectors and matrices, provides many algorithms to increase the accuracy and speed of computation for analyzing data and to discover the characteristics of a data set. These algorithms are very useful for understanding the computing process of probability, statistics and the learning machine. This book introduces many basics of linear algebra using Python packages numpy, sympy, and so on. Chapters 1 and 2 introduce the creation and characteristics of vectors and matrices. Chapter 3 describes the linear system(linear combination) through the process finding the solution in a system of simultaneous equations. Vector space, a concept introduced in Chapter 4, is used to infer the collective characteristics and relationships of each vector of a linear system. Chapter 5 introduces the coordinate system to represent the linear system geometrically. Chapter 6 introduces the process of transforming while maintaining basic characteristics such as vectors and matrices. Finally, Chapter 7 describes several ways to decompose the original form into a simple form. In this process, we use a variety of Python functions.

Basics Of Linear Algebra For Machine Learning

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Author : Jason Brownlee
language : en
Publisher: Machine Learning Mastery
Release Date : 2018-01-24

Basics Of Linear Algebra For Machine Learning written by Jason Brownlee and has been published by Machine Learning Mastery this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-01-24 with Computers categories.

Linear algebra is a pillar of machine learning. You cannot develop a deep understanding and application of machine learning without it. In this laser-focused Ebook, you will finally cut through the equations, Greek letters, and confusion, and discover the topics in linear algebra that you need to know. Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover what linear algebra is, the importance of linear algebra to machine learning, vector, and matrix operations, matrix factorization, principal component analysis, and much more.

Linear Algebra With Python

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Author : Htyun-seok Son
language : en
Publisher: Hyun-seok Son
Release Date : 2024-12-23

Linear Algebra With Python written by Htyun-seok Son and has been published by Hyun-seok Son this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-23 with Mathematics categories.

Python is one of the most popular languages for data analysis and prediction. Moreover, useful tools for deep learning, such as tensorflow and pytorch, which have recently been in the spotlight, are perfectly implemented in Python. In these languages, the basic form of data is an array, which is created by numpy, an important package in Python. In particular, the array type is the basis of data science because it has the structure of vectors and matrices that give direction and size to each value of the data set. For example, the matrix structure enables conversion to a simple form without losing the basic characteristics of a large data set. This conversion is useful for efficient processing of data and discovering implicit characteristics. Linear algebra, which provides the basic theory for utilizing vectors and matrices, provides many algorithms to increase the accuracy and speed of calculations in the computing process for data analysis and to discover the characteristics of the data set. These algorithms are very useful for understanding probability, statistics, and computing applied to learning machines. This book introduces many basic aspects of linear algebra using Python packages such as numpy and sympy. Chapters 1 and 2 introduce the creation and characteristics of vectors and matrices. These characteristics are advantageous for various characteristics and calculations of functions by connecting linear functions with vectors or matrices. This part is introduced in Chapter 3. The process of converting a function to another function while maintaining its basic characteristics is covered in Chapter 4. This transformation can convert and apply complex functions to a simpler form. This transformation is called decomposition and is introduced in Chapter 5. In this process, various Python functions and packages are applied and their descriptions are attached in the appendix.

Practical Linear Algebra For Data Science

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Author : Mike X Cohen
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2022-09-06

Practical Linear Algebra For Data Science written by Mike X Cohen and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-06 with Computers categories.

If you want to work in any computational or technical field, you need to understand linear algebra. As the study of matrices and operations acting upon them, linear algebra is the mathematical basis of nearly all algorithms and analyses implemented in computers. But the way it's presented in decades-old textbooks is much different from how professionals use linear algebra today to solve real-world modern applications. This practical guide from Mike X Cohen teaches the core concepts of linear algebra as implemented in Python, including how they're used in data science, machine learning, deep learning, computational simulations, and biomedical data processing applications. Armed with knowledge from this book, you'll be able to understand, implement, and adapt myriad modern analysis methods and algorithms. Ideal for practitioners and students using computer technology and algorithms, this book introduces you to: The interpretations and applications of vectors and matrices Matrix arithmetic (various multiplications and transformations) Independence, rank, and inverses Important decompositions used in applied linear algebra (including LU and QR) Eigendecomposition and singular value decomposition Applications including least-squares model fitting and principal components analysis

Linear Algebra For Data Science In Python

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Author : 365 Careers
language : en
Publisher:
Release Date : 2019

Linear Algebra For Data Science In Python written by 365 Careers and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.

Know all about Linear Algebra for Data Science in Python About This Video Learn linear algebra for data science and understand the essential concepts Understand matrix, scalars, and vectors and learn how to use them In Detail Vectorizing your code is an essential skill to make your calculations faster and take advantage of the capabilities of modern machine and deep learning packages. This course will get you up and running with linear algebra fundamentals for data science in Python. In this course, you will learn about scalars, vectors, and matrices and the geometrical meaning of these objects. You will also learn how you should use linear algebra in your Python code. In addition to this, you'll be able to perform operations such as addition, subtraction and dot product. As you cover further sections, you'll focus on the different syntactical errors you can encounter while vectorizing your code. By the end of this course, you will have gained the skills you need to use linear algebra confidently in your data science projects. Downloading the example code for this course: You can download the example code files for this course on GitHub at the following link: https://github.com/PacktPublishing/Linear-Algebra-for-Data-Science-in-Python . If you require support please email: [email protected].

Matrix Algebra

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Author : James E. Gentle
language : en
Publisher: Springer
Release Date : 2017-10-12

Matrix Algebra written by James E. Gentle and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-10-12 with Mathematics categories.

Matrix algebra is one of the most important areas of mathematics for data analysis and for statistical theory. This much-needed work presents the relevant aspects of the theory of matrix algebra for applications in statistics. It moves on to consider the various types of matrices encountered in statistics, such as projection matrices and positive definite matrices, and describes the special properties of those matrices. Finally, it covers numerical linear algebra, beginning with a discussion of the basics of numerical computations, and following up with accurate and efficient algorithms for factoring matrices, solving linear systems of equations, and extracting eigenvalues and eigenvectors.

Algebra And Geometry With Python

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Author : Sergei Kurgalin
language : en
Publisher: Springer Nature
Release Date : 2021-01-18

Algebra And Geometry With Python written by Sergei Kurgalin and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-18 with Computers categories.

This book teaches algebra and geometry. The authors dedicate chapters to the key issues of matrices, linear equations, matrix algorithms, vector spaces, lines, planes, second-order curves, and elliptic curves. The text is supported throughout with problems, and the authors have included source code in Python in the book. The book is suitable for advanced undergraduate and graduate students in computer science.

Practical Linear Algebra For Data Science

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Author : MIKE. COHEN
language : en
Publisher: O'Reilly Media
Release Date : 2022-11-30

Practical Linear Algebra For Data Science written by MIKE. COHEN and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-30 with Computers categories.

Python Programming For Biology

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Author : Tim J. Stevens
language : en
Publisher: Cambridge University Press
Release Date : 2015-02-12

Python Programming For Biology written by Tim J. Stevens and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-02-12 with Computers categories.

This book introduces Python as a powerful tool for the investigation of problems in computational biology, for novices and experienced programmers alike.

Linear Algebra With Python

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