Before Machine Learning Volume 1 – Linear Algebra

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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. Matrix and vector multiplication as well as Matrix matrix multiplication. Outer products. The inverse of a matrix. The Determinante. Systems of linear equations. Eigenvectors and eigenvalues. Eigen decomposition. The single value decomposition. The principal component analysis.

Details e-book Before Machine Learning Volume 1 - Linear Algebra

🗸 Author(s):
🗸 Title: Before Machine Learning Volume 1 - Linear Algebra
🗸 Rating : 4.5 from 5 stars (29 reviews)
🗸 Languange: English
🗸 Format ebook: PDF, EPUB, Kindle, Audio, HTML and MOBI
🗸 Supported Devices: Android, iOS, PC and Amazon Kindle


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