Shroff Publishers do not endorse the preview pages of kindle linked to our ISBNs. All Indian Reprints of Jorge Brazil Titles are Printed in Grayscale.
Why:
Linear algebra is a fundamental topic for anyone working in machine learning, and it plays a critical role in understanding the inner workings of algorithms and data models. In this book, you’ll learn how to apply linear algebra to real-world problems and gain a deep understanding of the concepts that drive machine learning.
What is different:
What sets this book apart is its different approach to teaching. Rather than presenting abstract mathematical concepts in isolation, the content is structured like a story with real-life examples that illustrate the practical applications of linear algebra. It is written in a conversational style as if you were having a one-on-one conversation with me, and the structure resembles a story.
To whom:
Whether you’re a beginner or an experienced practitioner, this book will help you master the essentials of linear algebra and build a solid foundation for your machine-learning journey. It assumes no prior knowledge of linear algebra, making it perfect for beginners. However, it also includes advanced concepts, making it a valuable resource for more experienced learners.
What's inside:
This book covers all the essential topics in linear algebra, from vectors and matrices to eigenvalues and eigenvectors. It also includes in-depth discussions of applications of linear algebra, such as principal component analysis, and single-value decomposition.
Vectors additi... See more
Shroff Publishers do not endorse the preview pages of kindle linked to our ISBNs. All Indian Reprints of Jorge Brazil Titles are Printed in Grayscale.
Why:
Linear algebra is a fundamental topic for anyone working in machine learning, and it plays a critical role in understanding the inner workings of algorithms and data models. In this book, you’ll learn how to apply linear algebra to real-world problems and gain a deep understanding of the concepts that drive machine learning.
What is different:
What sets this book apart is its different approach to teaching. Rather than presenting abstract mathematical concepts in isolation, the content is structured like a story with real-life examples that illustrate the practical applications of linear algebra. It is written in a conversational style as if you were having a one-on-one conversation with me, and the structure resembles a story.
To whom:
Whether you’re a beginner or an experienced practitioner, this book will help you master the essentials of linear algebra and build a solid foundation for your machine-learning journey. It assumes no prior knowledge of linear algebra, making it perfect for beginners. However, it also includes advanced concepts, making it a valuable resource for more experienced learners.
What's inside:
This book covers all the essential topics in linear algebra, from vectors and matrices to eigenvalues and eigenvectors. It also includes in-depth discussions of applications of linear algebra, such as principal component analysis, and single-value decomposition.
Vectors addition.
Multiplication of a vector by a scalar.
The dot product.
Vectors spaces, linear combinations, linear independence, and basis.
Change of basis.
