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.
