LEADER 03176nam 22004573 450 001 9910637694403321 005 20240415084506.0 010 $a1-63828-053-3 035 $a(CKB)5860000000282520 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/95746 035 $a(MiAaPQ)EBC30191446 035 $a(Au-PeEL)EBL30191446 035 $a(OCoLC)1492945352 035 $a(EXLCZ)995860000000282520 100 $a20240415d2022 uy 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aConvex Optimization for Machine Learning 205 $a1st ed. 210 1$aNorwell, MA :$cNow Publishers,$d2022. 210 4$dİ2022. 215 $a1 electronic resource (379 p.) 225 1 $aNowOpen 311 08$a1-63828-052-5 330 $aThis book covers an introduction to convex optimization, one of the powerful and tractable optimization problems that can be efficiently solved on a computer. The goal of the book is to help develop a sense of what convex optimization is, and how it can be used in a widening array of practical contexts with a particular emphasis on machine learning. The first part of the book covers core concepts of convex sets, convex functions, and related basic definitions that serve understanding convex optimization and its corresponding models. The second part deals with one very useful theory, called duality, which enables us to: (1) gain algorithmic insights; and (2) obtain an approximate solution to non-convex optimization problems which are often difficult to solve. The last part focuses on modern applications in machine learning and deep learning. A defining feature of this book is that it succinctly relates the ?story? of how convex optimization plays a role, via historical examples and trending machine learning applications. Another key feature is that it includes programming implementation of a variety of machine learning algorithms inspired by optimization fundamentals, together with a brief tutorial of the used programming tools. The implementation is based on Python, CVXPY, and TensorFlow. This book does not follow a traditional textbook-style organization, but is streamlined via a series of lecture notes that are intimately related, centered around coherent themes and concepts. It serves as a textbook mainly for a senior-level undergraduate course, yet is also suitable for a first-year graduate course. Readers benefit from having a good background in linear algebra, some exposure to probability, and basic familiarity with Python. 410 0$aNowOpen Series 606 $aOptimization$2bicssc 610 $aConvex Optimization, Deep Learning, Generative Adversarial Networks (GANs), TensorFlow, Supervised Learning, Wasserstein GAN, Strong Duality, Weak Duality, Computed Tomography 615 7$aOptimization 676 $a006.31 700 $aSuh$b Changho$01310449 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910637694403321 996 $aConvex Optimization for Machine Learning$93029832 997 $aUNINA