Adaptive machine learning algorithms with python : solve data analytics and machine learning problems on edge devices

1. Introduction 2. General Theories and Notations 3. Square Root and Inverse Square Root 4. First Principal Eigenvector 5. Principal and Minor Eigenvectors 6. Accelerated Computation of Eigenvectors 7. Generalized Eigenvectors 8. Real-World Applications of Adaptive Linear Algorithms

This book teaches you how to use adaptive algorithms to solve streaming data problems in low-resource environments. It covers topics such as matrix functions, eigenvectors, generalized eigenvectors, and their applications to machine learning and data analysis. It also provides a common framework for creating and implementing adaptive algorithms, and shows real-world examples and code snippets. The book is suitable for machine learning engineers, data scientists, and software architects who want to learn how to handle time-varying, non-stationary data with minimal compute, memory, latency, and bandwidth.