| |
|
|
|
|
|
|
|
|
1. |
Record Nr. |
UNINA9910552714203321 |
|
|
Autore |
Chatterjee Chanchal |
|
|
Titolo |
Adaptive Machine Learning Algorithms with Python : Solve Data Analytics and Machine Learning Problems on Edge Devices / / by Chanchal Chatterjee |
|
|
|
|
|
|
|
Pubbl/distr/stampa |
|
|
Berkeley, CA : , : Apress : , : Imprint : Apress, , 2022 |
|
|
|
|
|
|
|
ISBN |
|
|
|
|
|
|
|
|
Edizione |
[1st ed. 2022.] |
|
|
|
|
|
Descrizione fisica |
|
1 online resource (290 pages) |
|
|
|
|
|
|
Disciplina |
|
|
|
|
|
|
Soggetti |
|
Python (Computer program language) |
Machine learning |
|
|
|
|
|
|
|
|
Lingua di pubblicazione |
|
|
|
|
|
|
Formato |
Materiale a stampa |
|
|
|
|
|
Livello bibliografico |
Monografia |
|
|
|
|
|
Nota di bibliografia |
|
Includes bibliographical references and index. |
|
|
|
|
|
|
Nota di contenuto |
|
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. |
|
|
|
|
|
|
|
|
Sommario/riassunto |
|
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. You will: 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. |
|
|
|
|
|
| |