Optimization techniques are at the core of data science, including data analysis and machine learning. An understanding of basic optimization techniques and their fundamental properties provides important grounding for students, researchers, and practitioners in these areas. This text covers the fundamentals of optimization algorithms in a compact, self-contained way, focusing on the techniques most
Popis produktu
Optimization techniques are at the core of data science, including data analysis and machine learning. An understanding of basic optimization techniques and their fundamental properties provides important grounding for students, researchers, and practitioners in these areas. This text covers the fundamentals of optimization algorithms in a compact, self-contained way, focusing on the techniques most relevant to data science. An introductory chapter demonstrates that many standard problems in data science can be formulated as optimization problems. Next, many fundamental methods in optimization are described and analyzed, including gradient and accelerated gradient methods for unconstrained optimization of smooth especially convex functions the stochastic gradient method, a workhorse algorithm in machine learning the coordinate descent approach several key algorithms for constrained optimization problems algorithms for minimizing nonsmooth functions arising in data science foundations of the analysis of nonsmooth functions and optimization duality and the back-propagation approach, relevant to neural networks.
543543
Samozrejme! Môžete mi prosím poskytnúť viac informácií o podkategórii 543543 a jej charakteristikách? Aké produkty alebo služby zahŕňa, aké sú jej výhody a aké publikum by malo byť cieľom? Tieto detaily by mi pomohli vytvoriť presnejší a relevantnejší popis.