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Mathematics for Machine Learning: A Deep Dive into Algorithms by N. Sahu is a nonfiction available to read on EtoBox.

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Title Page Copyright Page Mathematics for Machine Learning: A Deep Dive into Algorithms Preface The Journey Ahead Getting the Most from This Book Let's Begin Table of Contents Book Summary Key Features Your Journey Begins Here Mathematics for Machine Learning: A Deep Dive into Algorithms | Nibedita Sahu Introduction | The Role of Mathematics in Machine Learning Prerequisites for the Book How to Use This Book Effectively Chapter 1: Foundations of Linear Algebra 1.1. Vectors and Matrices 1.2 Matrix Operations 1.3 Vector Spaces and Linear Transformations 1.4 Eigenvalues and Eigenvectors Chapter 2: Multivariable Calculus 2.1 Partial Derivatives 2.2 Gradients and Jacobian Matrices 2.3 Chain Rule and Higher-Order Derivatives 2.4 Optimization Techniques Chapter 3: Probability and Statistics 3.1 Basic Probability Concepts 3.2 Random Variables and Probability Distributions 3.3 Expectation, Variance, and Covariance 3.4 Maximum Likelihood Estimation Chapter 4: Information Theory 4.1 Entropy and Information Gain 4.2 Kullback-Leibler Divergence 4.3 Mutual Information and Applications Chapter 5: Linear Regression 5.1 Simple Linear Regression 5.2 Multiple Linear Regression 5.3 Least Squares Esti

Who reads Mathematics for Machine Learning: A Deep Dive into Algorithms?

Author

N. Sahu

Published

2023

Language

EN

Category

nonfiction