LEADER 03677nam 22005415 450 001 9910552714203321 005 20230804135806.0 010 $a9781484280171 010 $a1484280172 024 7 $a10.1007/978-1-4842-8017-1 035 $a(MiAaPQ)EBC6921844 035 $a(Au-PeEL)EBL6921844 035 $a(CKB)21393290800041 035 $a(OCoLC)1303571868 035 $a(OCoLC-P)1303571868 035 $a(DE-He213)978-1-4842-8017-1 035 $a(PPN)261525093 035 $a(CaSebORM)9781484280171 035 $a(Perlego)4513889 035 $a(EXLCZ)9921393290800041 100 $a20220312d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdaptive Machine Learning Algorithms with Python $eSolve Data Analytics and Machine Learning Problems on Edge Devices /$fby Chanchal Chatterjee 205 $a1st ed. 2022. 210 1$aBerkeley, CA :$cApress :$cImprint: Apress,$d2022. 215 $a1 online resource (290 pages) 311 08$a9781484280164 311 08$a1484280164 320 $aIncludes bibliographical references and index. 327 $aChapter 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. 330 $aLearn 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. 606 $aPython (Computer program language) 606 $aMachine learning 615 0$aPython (Computer program language) 615 0$aMachine learning. 676 $a005.133 700 $aChatterjee$b Chanchal$01214060 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910552714203321 996 $aAdaptive Machine Learning Algorithms with Python$92803792 997 $aUNINA