Machine learning with Pytorch and Sscikit-Learn: develop machine learning and deep learning models with scikit-learn and PyTorch
This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch's simple to code framework. Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features Learn applied machine learning...
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| Körperschaft: | |
| Weitere beteiligte Personen: | , , |
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Birmingham
Packt Publishing, Limited
2022
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| Links: | https://learning.oreilly.com/library/view/-/9781801819312/?ar |
| Zusammenfassung: | This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch's simple to code framework. Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features Learn applied machine learning with a solid foundation in theory Clear, intuitive explanations take you deep into the theory and practice of Python machine learning Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices Book DescriptionMachine 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). This PyTorch book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments. What you will learn Explore frameworks, models, and techniques for machines to 'learn' from data Use scikit-learn for machine learning and PyTorch for deep learning Train machine learning classifiers on images, text, and more Build and train neural networks, transformers, and boosting algorithms Discover best practices for evaluating and tuning models Predict continuous target outcomes using regression analysis Dig deeper into textual and social media data using sentiment analysis Who this book is for If you have a good grasp of Python basics and want to start learning about machine learning and deep learning, then this is the book for you. This is an essential resource written for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch. Before you get started with this book, you'll need a good understanding of calculus, as well as linear algebra. |
| Beschreibung: | Available through Ebscohost. - Description based on resource viewed on September 18, 2023 |
| Umfang: | 1 Online-Ressource (771 Seiten) |
| ISBN: | 1801816387 9781801816380 9781801819312 |
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| spelling | Raschka, Sebastian VerfasserIn aut Machine learning with Pytorch and Sscikit-Learn develop machine learning and deep learning models with scikit-learn and PyTorch Sebastian Raschka, Yuxi (Hayden) Liu, Vahid Mirjalili Birmingham Packt Publishing, Limited 2022 1 Online-Ressource (771 Seiten) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Available through Ebscohost. - Description based on resource viewed on September 18, 2023 This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch's simple to code framework. Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features Learn applied machine learning with a solid foundation in theory Clear, intuitive explanations take you deep into the theory and practice of Python machine learning Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices Book DescriptionMachine 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). This PyTorch book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments. What you will learn Explore frameworks, models, and techniques for machines to 'learn' from data Use scikit-learn for machine learning and PyTorch for deep learning Train machine learning classifiers on images, text, and more Build and train neural networks, transformers, and boosting algorithms Discover best practices for evaluating and tuning models Predict continuous target outcomes using regression analysis Dig deeper into textual and social media data using sentiment analysis Who this book is for If you have a good grasp of Python basics and want to start learning about machine learning and deep learning, then this is the book for you. This is an essential resource written for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch. Before you get started with this book, you'll need a good understanding of calculus, as well as linear algebra. Python (Computer program language) Machine learning Data mining Data Mining Machine Learning Python (Langage de programmation) Apprentissage automatique Exploration de données (Informatique) Liu, Yuxi (Hayden) MitwirkendeR ctb Mirjalili, Vahid MitwirkendeR ctb Dzhulgakov, Dmytro MitwirkendeR ctb ProQuest (Firm) MitwirkendeR ctb |
| spellingShingle | Raschka, Sebastian Machine learning with Pytorch and Sscikit-Learn develop machine learning and deep learning models with scikit-learn and PyTorch Python (Computer program language) Machine learning Data mining Data Mining Machine Learning Python (Langage de programmation) Apprentissage automatique Exploration de données (Informatique) |
| title | Machine learning with Pytorch and Sscikit-Learn develop machine learning and deep learning models with scikit-learn and PyTorch |
| title_auth | Machine learning with Pytorch and Sscikit-Learn develop machine learning and deep learning models with scikit-learn and PyTorch |
| title_exact_search | Machine learning with Pytorch and Sscikit-Learn develop machine learning and deep learning models with scikit-learn and PyTorch |
| title_full | Machine learning with Pytorch and Sscikit-Learn develop machine learning and deep learning models with scikit-learn and PyTorch Sebastian Raschka, Yuxi (Hayden) Liu, Vahid Mirjalili |
| title_fullStr | Machine learning with Pytorch and Sscikit-Learn develop machine learning and deep learning models with scikit-learn and PyTorch Sebastian Raschka, Yuxi (Hayden) Liu, Vahid Mirjalili |
| title_full_unstemmed | Machine learning with Pytorch and Sscikit-Learn develop machine learning and deep learning models with scikit-learn and PyTorch Sebastian Raschka, Yuxi (Hayden) Liu, Vahid Mirjalili |
| title_short | Machine learning with Pytorch and Sscikit-Learn |
| title_sort | machine learning with pytorch and sscikit learn develop machine learning and deep learning models with scikit learn and pytorch |
| title_sub | develop machine learning and deep learning models with scikit-learn and PyTorch |
| topic | Python (Computer program language) Machine learning Data mining Data Mining Machine Learning Python (Langage de programmation) Apprentissage automatique Exploration de données (Informatique) |
| topic_facet | Python (Computer program language) Machine learning Data mining Data Mining Machine Learning Python (Langage de programmation) Apprentissage automatique Exploration de données (Informatique) |
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