Data science and machine learning have been deeply integrated into every aspect of our lives, and mathematics is the key to unlocking the future. Not everyone is born with a good deck of cards, but mastering "mathematics + programming + machine learning" is definitely a trump card. This time, learning mathematics is no longer about exams, scores, and higher education, but about investing time, self-realization, and facing the future. In order to help everyone learn mathematics, use mathematics, and even fall in love with mathematics, when creating this set of books, the author tried his best to overcome the various shortcomings of traditional mathematics textbooks, so that everyone can be interested, understand, think, and be more confident and useful when learning.
Iris Book has three major sections - programming, mathematics, and practice. Various algorithms in data science and machine learning are inseparable from mathematics. This book "The Power of Matrix" is the second book in the "Mathematics" section and mainly introduces commonly used linear algebra tools. Any mathematical tool that wants to extend from univariate to multivariate, such as multivariate calculus and multivariate statistics, cannot avoid linear algebra.
"The Power of Matrix: Linear Algebra Full-Color Illustrations + Micro Courses + Python Programming" has a total of 25 chapters, which can be summarized into 7 major sections: vectors, matrices, vector spaces, matrix decomposition, calculus, space geometry, and data. "The Power of Matrix: Linear Algebra Full-Color Illustrations + Micro Courses + Python Programming" will explain linear algebra tools while introducing their application scenarios in the fields of data science and machine learning, allowing everyone to apply what they have learned. The readership of "The Power of Matrix: Linear Algebra Full-Color Illustrations + Micro Courses + Python Programming" includes all friends who apply mathematics at work. It is especially suitable for junior programmers to advance, undergraduates to gain understanding of mathematics, senior data analysts, and artificial intelligence developers.
Reader comments
This book is full of pictures and texts. After reading only the first chapter, I got a refreshing feeling: it gives an intuitive expression of the concept, it is no longer a simple frozen expression of the definition, it seems to have warmth [laughs]
The cases in the book are close to reality, and you can apply mathematical knowledge through Python programming and apply what you have learned. Moreover, it is also interspersed with application scenarios in the fields of data science and machine learning, which is very suitable for friends who need to master linear algebra. I particularly like the vector and matrix sections, which are explained very clearly. All in all, recommended!
Combined with images, it is concise and clear, making it easy to learn.
This book is full of pictures and texts. After reading only the first chapter, I got a refreshing feeling: it gives an intuitive expression of the concept, it is no longer a simple frozen expression of the definition, it seems to have warmth [laughs]
The cases in the book are close to reality, and you can apply mathematical knowledge through Python programming and apply what you have learned. Moreover, it is also interspersed with application scenarios in the fields of data science and machine learning, which is very suitable for friends who need to master linear algebra. I particularly like the vector and matrix sections, which are explained very clearly. All in all, recommended!
Combined with images, it is concise and clear, making it easy to learn.
Easy to understand and very good
Content is colorful and easy to understand