LEADER 04332nam a22004457i 4500001 99131265443906421 005 20241002170935.4 006 m o d 007 cr ||||||||||| 008 241001t20242024kz a ob 001 0 rus d 020 9786011100342 020 6011100341 035 (CaSebORM)9786011100342 035 (CKB)36267987000041 035 (OCoLC)1458319864 035 (OCoLC-P)1458319864 035 (EXLCZ)9936267987000041 040 OCoLC-P |beng |erda |epn |cOCoLC-P 050 4 QA76.73.P98 082 04 005.13/3 |223/eng/20241001 100 1 Raschka, Sebastian, |eauthor. |1https://id.oclc.org/worldcat/entity/E39PBJwMGYGbcd3GgvBd9B7T73 240 10 Machine learning with Pytorch and Scikit-Learn. |lRussian 245 10 Mashinnoe obuchenie s Pytorch i Scikit-Learn / |cSebastʹi︠a︡n Rashka, I︠U︡si (Khėĭden) Li︠u︡, Vakhid Mirdzhalili. 264 1 Astana [Kazakhstan] : |bIzdatelʹstvo Foliant, |c[2024] 264 4 |c©2024 300 1 online resource (688 pages) : |billustrations 336 text |btxt |2rdacontent 337 computer |bc |2rdamedia 338 online resource |bcr |2rdacarrier 588 OCLC-licensed vendor bibliographic record. 520 Ischerpyvai︠u︡shchee rukovodstvo po mashinnomu (MO) i glubokomu obuchenii︠u︡ s ispolʹzovaniem i︠a︡zyka programmirovanii︠a︡ Python, freĭmvorka PyTorch i biblioteki scikit-learn. Rassmotreny osnovy MO, algoritmy dli︠a︡ zadach klassifikat︠s︡ii, klassifikatory na osnove scikit-learn, predvaritelʹnai︠a︡ obrabotka i szhatie dannykh, sovremennye metody ot︠s︡enki modeleĭ i obʺedinenie razlichnykh modeleĭ dli︠a︡ ansamblevogo obuchenii︠a︡. Rasskazano o primenenii MO dli︠a︡ analiza teksta i prognozirovanii nepreryvnykh t︠s︡elevykh peremennykh s pomoshchʹi︠u︡ regressionnogo analiza, klasternom analize i obuchenii bez uchiteli︠a︡, pokazano postroenie mnogosloĭnoĭ iskusstvennoĭ neĭronnoĭ seti s nuli︠a︡. Raskryty prodvinutye vozmozhnosti PyTorch dli︠a︡ reshenii︠a︡ slozhnykh zadach. Opisano primenenie glubokikh svertochnykh i rekurrentnykh neĭronnykh seteĭ, transformerov, generativnykh sosti︠a︡zatelʹnykh i grafovykh neĭronnykh seteĭ, Osoboe vnimanie udeleno obuchenii︠u︡ s podkrepleniem dli︠a︡ sistem prini︠a︡tii︠a︡ resheniĭ v slozhnykh sredakh. Ėlektronnyĭ arkhiv soderzhit t︠s︡vetnye illi︠u︡strat︠s︡ii i kody vsekh primerov. Dli︠a︡ programmistov v oblasti mashinnogo obuchenii︠a︡. 520 Machine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself. Why PyTorch? PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric. You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP). 500 Translated from the English. 650 0 Python (Computer program language) 650 0 Machine learning. 650 0 Deep learning (Machine learning) 650 0 Data mining. 700 1 Liu, Yuxi |c(Data scientist), |eauthor. 700 1 Mirjalili, Vahid, |eauthor. |1https://id.oclc.org/worldcat/entity/E39PCjDvgjQKwQh8R8RYP8pYrm 906 BOOK