000			02358 a2200169 4500
008			231122b |||||||| |||| 00| 0 eng d
020			_a9789355424402
082			_a005.74015 _bBRA
100			_aBrasil, Jorge
245			_aBefore Machine Learning - Volume 1: Linear Algebra for A.I
260			_bShroff Publishers & Distributors Pvt. Ltd. _c2023 _aMumbai
300			_a164 p.
520			_aWhy: 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.
650			_aMachine Learning
906			_aBusiness Analytics
942			_c1 _2ddc
999			_c98108 _d98108