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142608441
Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python Paperback – 25 Feb. 2022
Lambar abu:
142608441
XAF 48464
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Machine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch.
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Bayanin samfur
| Publisher | Packt Publishing |
| Publication date | 25 Feb. 2022 |
| Language | English |
| Print length | 770 pages |
| ISBN-10 | 1801819319 |
| ISBN-13 | 978-1801819312 |
| Dimensions | 19.05 x 4.45 x 23.5 cm |
| Country of Origin | This item will be imported from EU |
| What is in the box | Machine Learning with PyTorch... For more details, please check description/product details |
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Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python Paperback – 25 Feb. 2022
About This Item
Are you looking to delve into the fascinating field of machine learning? Look no further than Machine Learning with PyTorch and Scikit-Learn. This comprehensive guide combines the power of two leading Python libraries, PyTorch and Scikit-Learn, to help you develop and implement cutting-edge machine learning and deep learning models. With a practical and hands-on approach, this book is perfect for beginners and experienced data scientists alike. Whether you're just starting out or looking to expand your knowledge, you'll find valuable insights and techniques to enhance your machine learning skills. PyTorch, known for its flexibility and ease of use, forms the backbone of this book.
You'll learn how to build and train machine learning models using PyTorch's intuitive interface and powerful computational capabilities. Dive into the world of deep learning as you explore neural networks, convolutional networks, recurrent networks, and more. But that's not all – we also bring in the power of Scikit-Learn, another renowned machine learning library. By integrating Scikit-Learn with PyTorch, you'll have access to a wider range of algorithms and frameworks for solving complex real-world problems.
From classification and regression to clustering and dimensionality reduction, this book covers it all. Throughout the book, you'll find practical examples and code snippets that illustrate key concepts and techniques. From building your own machine learning projects to implementing natural language processing and tackling advanced topics, this book will equip you with the skills you need to excel in the field of machine learning. Don't miss out on the opportunity to become a machine learning expert. Get your copy of Machine Learning with PyTorch and Scikit-Learn today and embark on an exciting journey into the world of data science and artificial intelligence.
Kuna da wata tambaya? Yi taɗi da mu
Tambayoyi & Amsoshi na Abokan Ciniki
-
Tambaya:
What are the essential parts of PyTorch?
Amsa: The book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric. -
Tambaya:
What are the latest trends in deep learning covered in the book?
Amsa: 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). -
Tambaya:
Who is the book for?
Amsa: This book is for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch.
Bita na Edita
Ba a sami sake dubawa na edita ba
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Faɗan wa sauran Abokan ciniki ra'ayin ka
Ribobi
- Clear and concise explanations
- Hands-on coding examples
- Covers both ML and DL
- Great for beginners and pros
- Up-to-date with latest tools
Fursunoni
- Could include more advanced topics.
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XAF 48464
Wannan samfurin ba Ubuy bane suke sayar wa, kuma zai iya ɗaukar a ƙalla kwana goma kafin a kawo. Za mu iya soke samfurin daga oda kuma mu mayar da ku idan wata matsala ta taso game da isar da wannan samfurin.
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Fasaloli & Fa'idoji
- Learn applied machine learning with a solid foundation in theory
- Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices
- Teaches principles allowing you to build models and applications for yourself
- Companion to machine learning with Python
- For developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch