Adaptive Machine Learning Algorithms with Python

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 For Machine learning engineers, data scientist and architects, software engineers and architects handling edge device computation and data management.

Learn to use algorithms to solve machine learning and data analytics problems with low power and memory usage Create new algorithms for real-time machine learning use cases Implement code with adaptive algorithms for real world use cases

Auteur
Chanchal Chatterjee, Ph.D, has held several leadership roles in machine learning, deep learning and real-time analytics. He is currently leading Machine Learning and Artificial Intelligence at Google Cloud Platform, California, USA. Previously, he was the Chief Architect of EMC CTO Office where he led end-to-end deep learning and machine learning solutions for data centers, smart buildings, and smart manufacturing for leading customers. Chanchal received several awards including an Outstanding paper award from IEEE Neural Network Council for adaptive learning algorithms recommended by MIT professor Marvin Minsky. Chanchal founded two tech startups between 2008-2013. Chanchal has 29 granted or pending patents, and over 30 publications. Chanchal received M.S. and Ph.D. degrees in Electrical and Computer Engineering from Purdue University.

Contenu
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.