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Full Description
Learn to use adaptive algorithms to solve real-world streaming data problems. This book covers a multitude of data processing challenges, ranging from the simple to the complex. At each step, you will gain insight into real-world use cases, find solutions, explore code used to solve these problems, and create new algorithms for your own use.
Authors Chanchal Chatterjee and Vwani P. Roychowdhury begin by introducing a common framework for creating adaptive algorithms, and demonstrating how to use it to address various streaming data issues. Examples range from using matrix functions to solve machine learning and data analysis problems to more critical edge computation problems. They handle time-varying, non-stationary data with minimal compute, memory, latency, and bandwidth.
Upon finishing this book, you will have a solid understanding of how to solve adaptive machine learning and data analytics problems and be able to derive new algorithms for your own use cases. You will also come away with solutions to high volume time-varying data with high dimensionality in a low compute, low latency environment.
What You Will Learn
Apply adaptive algorithms to practical applications and examples
Understand the relevant data representation features and computational models for time-varying multi-dimensional data
Derive adaptive algorithms for mean, median, covariance, eigenvectors (PCA) and generalized eigenvectors with experiments on real data
Speed up your algorithms and put them to use on real-world stationary and non-stationary data
Master the applications of adaptive algorithms on critical edge device computation applications
Who This Book Is ForMachine learning engineers, data scientist and architects, software engineers and architects handling edge device computation and data management.
Contents
Chapter 1. Introducing Data Representation Features.- Chapter 2. General Theories and Notations.- Chapter 3. Square Root and Inverse Square Root.- Chapter 4. First Principal Eigenvector.- Chapter 5. Principal and Minor Eigenvectors.- Chapter 6. Accelerated Computation eigenvectors.- Chapter 7. Generalized Eigenvectors.- Chapter 8. Real - World Applications Linear Algorithms.
