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Tuotteen numero:
47529177
Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python
Tuotteen numero:
47529177
€ 68
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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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Mikä erottuu joukosta
Comprehensive Guide
Offers an in-depth exploration of both machine learning and deep learning concepts, providing readers with a thorough understanding necessary to build sophisticated models using Python.
Käytännön sovellukset
Includes hands-on projects utilizing PyTorch and Scikit-Learn, allowing readers to apply theoretical knowledge to real-world problems, enhancing learning through practice.
Accessible for All
Written for beginners and experienced developers alike, the book demystifies complex topics, making advanced concepts in machine learning approachable for all skill levels.
Tuotteen yksityiskohdat
- 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 FeaturesLearn applied machine learning with a solid foundation in theoryClear, intuitive explanations take you deep into the theory and practice of Python machine learningFully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practicesBook 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 learnExplore frameworks, models, and techniques for machines to 'learn' from dataUse scikit-learn for machine learning and PyTorch for deep learningTrain machine learning classifiers on images, text, and moreBuild and train neural networks, transformers, and boosting algorithmsDiscover best practices for evaluating and tuning modelsPredict continuous target outcomes using regression analysisDig deeper into textual and social media data using sentiment analysisWho this book is forIf 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.Table of ContentsGiving Computers the Ability to Learn from DataTraining Simple Machine Learning Algorithms for ClassificationA Tour of Machine Learning Classifiers Using Scikit-LearnBuilding Good Training Datasets – Data PreprocessingCompressing Data via Dimensionality ReductionLearning Best Practices for Model Evaluation and Hyperparameter TuningCombining Different Models for Ensemble LearningApplying Machine Learning to Sentiment AnalysisPredicting Continuous Target Variables with Regression AnalysisWorking with Unlabeled Data – Clustering AnalysisImplementing a Multilayer Artificial Neural Network from Scratch(N.B. Please use the Look Inside option to see further chapters)
| kustantaja | Packt Publishing |
| Julkaisupäivä | 25 Feb. 2022 |
| Kieli | Englanti |
| Tulostuksen pituus | 770 pages |
| ISBN-10 | 1801819319 |
| ISBN-13 | 978-1801819312 |
| Tuotteen paino | 1.4 kg |
| mitat | 19.05 x 4.45 x 23.5 cm |
Kenelle sopii?
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Beginners in ML
Ideal for individuals new to machine learning, providing step-by-step guidance and foundational knowledge.
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Python Developers
Perfect for Python developers looking to enhance their skills in machine learning and deep learning frameworks.
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Data Scientists
Useful for data scientists who want to implement machine learning algorithms using PyTorch and Scikit-Learn.
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Advanced Practitioners
Not suitable for seasoned experts seeking advanced techniques or in-depth theoretical explorations of machine learning.
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Ei-tekniset käyttäjät
May be challenging for individuals without a programming background or basic understanding of machine learning concepts.
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Niche Applications
Users looking for highly specialized machine learning applications might not find the content adequately focused.
TUOTEKUVAUS.
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.
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Asiakkaiden kysymykset ja vastaukset
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kysymys:
What are the essential parts of PyTorch?
vastaus: The book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric. -
kysymys:
What are the latest trends in deep learning covered in the book?
vastaus: 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). -
kysymys:
Who is the book for?
vastaus: This book is for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch.
Higher Education Editorial Review
The book "Machine Learning with PyTorch and Scikit-Learn" has received overwhelmingly positive customer feedback. The book is Considered comprehensive and detailed, covering a wide range of topics in-depth, making it suitable for experienced developers as well as those new to the field. The inclusion of recent technologies such as transformers and GANs, as well as coverage of PyTorch and Scikit-Learn, is praised. Customers appreciate the balance between theory and practical applications, as well as the clear and detailed explanations. The book is seen as a reference book due to its extensive content, with readers encouraged to use it as a resource rather than trying to digest it all at once. Some readers have particularly highlighted the chapter on Transformers and NLP as exceptionally well-written and informative. A key point of criticism is the physical quality of the book, with readers noting wavy pages and black and white print. However, the availability of a free eBook download mitigates this issue. Overall, the book is recommended for anyone looking for a comprehensive introduction to machine learning, particularly for those interested in PyTorch. It is seen as an essential read and a valuable reference for further study. **
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Plussat
- Comprehensive and detailed coverage of various machine learning topics
- Inclusion of recent technologies such as transformers and GANs
- Clear and detailed explanations suitable for both experienced developers and beginners
- Balances theory and practical applications well
- Extensive coding samples for hands-on learning
- Valuable reference book for further study
Miinukset
- Physical quality of the book, including wavy pages and black and white print
Tuotteen hintahistoria
Tärkeitä tietoja
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€ 68
Tee tilaus nyt, niin saat tuotteen noin Monday, Kesäkuu 29
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Suojattu tapahtuma
Ubuy tekee kovasti töitä turvallisuutesi ja yksityisyytesi suojaamiseksi. Kehittynyt maksun turvallisuusjärjestelmämme varmistaa luottamuksellisuuden salaamalla tietosi siirron aikana AES (Advanced Encryption Standards)- ja SSL (Secure Socket Layer) -protokollilla. Maksutietosi ovat 100 % turvassa, sillä emme jaa maksutietojasi kolmansien osapuolten myyjille.
Tärkeimmät logistiikkakumppanimme
Nopein toimitus toiseen maahan
Ominaisuudet ja hyödyt
- 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