Mashinnoe obuchenie s Pytorch i Scikit-Learn:
Машинное обучение с Pytorch и Scikit-Learn /
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...
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| Main Authors: | , , |
|---|---|
| Format: | Electronic eBook |
| Language: | Russian |
| Published: |
Astana [Kazakhstan]
Izdatelʹstvo Foliant
[2024]
|
| Subjects: | |
| Links: | https://learning.oreilly.com/library/view/-/9786011100342/?ar |
| Summary: | 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. 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). |
| Item Description: | Translated from the English. - Includes bibliographical references and index |
| Physical Description: | 1 Online-Ressource (688 Seiten) Illustrationen |
| ISBN: | 9786011100342 6011100341 |
Staff View
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| 520 | |a 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. | ||
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| spelling | Raschka, Sebastian VerfasserIn aut Machine learning with Pytorch and Scikit-Learn 880-01 Mashinnoe obuchenie s Pytorch i Scikit-Learn Sebastʹi︠a︡n Rashka, I︠U︡si (Khėĭden) Li︠u︡, Vakhid Mirdzhalili 880-02 Astana [Kazakhstan] Izdatelʹstvo Foliant [2024] ©2024 1 Online-Ressource (688 Seiten) Illustrationen Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Translated from the English. - Includes bibliographical references and index 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. 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). Python (Computer program language) Machine learning Deep learning (Machine learning) Data mining Python (Langage de programmation) Apprentissage automatique Apprentissage profond Exploration de données (Informatique) Liu, Yuxi VerfasserIn aut Mirjalili, Vahid VerfasserIn aut 245-01/Cyrl Машинное обучение с Pytorch и Scikit-Learn / Себастьян Рашка, Юси (Хэйден) Лю, Вахид Мирджалили. 264-02/Cyrl Астана [Kazakhstan] Издательство Foliant [2024] |
| spellingShingle | Raschka, Sebastian Liu, Yuxi Mirjalili, Vahid Mashinnoe obuchenie s Pytorch i Scikit-Learn Python (Computer program language) Machine learning Deep learning (Machine learning) Data mining Python (Langage de programmation) Apprentissage automatique Apprentissage profond Exploration de données (Informatique) |
| title | Mashinnoe obuchenie s Pytorch i Scikit-Learn |
| title_alt | Machine learning with Pytorch and Scikit-Learn |
| title_auth | Mashinnoe obuchenie s Pytorch i Scikit-Learn |
| title_exact_search | Mashinnoe obuchenie s Pytorch i Scikit-Learn |
| title_full | Mashinnoe obuchenie s Pytorch i Scikit-Learn Sebastʹi︠a︡n Rashka, I︠U︡si (Khėĭden) Li︠u︡, Vakhid Mirdzhalili |
| title_fullStr | Mashinnoe obuchenie s Pytorch i Scikit-Learn Sebastʹi︠a︡n Rashka, I︠U︡si (Khėĭden) Li︠u︡, Vakhid Mirdzhalili |
| title_full_unstemmed | Mashinnoe obuchenie s Pytorch i Scikit-Learn Sebastʹi︠a︡n Rashka, I︠U︡si (Khėĭden) Li︠u︡, Vakhid Mirdzhalili |
| title_short | Mashinnoe obuchenie s Pytorch i Scikit-Learn |
| title_sort | mashinnoe obuchenie s pytorch i scikit learn |
| topic | Python (Computer program language) Machine learning Deep learning (Machine learning) Data mining Python (Langage de programmation) Apprentissage automatique Apprentissage profond Exploration de données (Informatique) |
| topic_facet | Python (Computer program language) Machine learning Deep learning (Machine learning) Data mining Python (Langage de programmation) Apprentissage automatique Apprentissage profond Exploration de données (Informatique) |
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