Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python Paperback – 25 Feb. 2022
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Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python Paperback – 25 Feb. 2022
Machine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch.
Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python Paperback – 25 Feb. 2022
Numéro d'article: 47529177

Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python Paperback – 25 Feb. 2022

Numéro d'article: 47529177

XOF 27325

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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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Ce qui se démarque

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.
Practical Applications
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.

Détails du produit

Explore machine learning and deep learning models with Python. Buy the paperback edition of Machine Learning with PyTorch and Scikit-Learn from the Ubuy Senegal Global Store.
Item Weight1.5 lbs (680 grams)

À qui est-ce destiné ?

Suitable For
  • Beginners in ML

    Ideal for individuals new to machine learning, providing step-by-step guidance and foundational knowledge.

  • Python Developers

    Perfect for Python developers looking to enhance their skills in machine learning and deep learning frameworks.

  • Data Scientists

    Useful for data scientists who want to implement machine learning algorithms using PyTorch and Scikit-Learn.

Not Suitable For
  • Advanced Practitioners

    Not suitable for seasoned experts seeking advanced techniques or in-depth theoretical explorations of machine learning.

  • Non-Technical Users

    May be challenging for individuals without a programming background or basic understanding of machine learning concepts.

  • Niche Applications

    Users looking for highly specialized machine learning applications might not find the content adequately focused.

DESCRIPTION DU PRODUIT

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.

Vous avez une question ? Chattez avec nous

Questions et réponses des clients

  • question: What are the essential parts of PyTorch?

    répondre: The book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric.
  • question: What are the latest trends in deep learning covered in the book?

    répondre: 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).
  • question: Who is the book for?

    répondre: 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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Avantages

  • 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

Les inconvénients

  • Physical quality of the book, including wavy pages and black and white print

Historique des prix du produit

Informations importantes

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XOF 27325

Commandez maintenant et recevez votre commande aux alentours du Sunday, Juin 28

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Ubuy s'engage à protéger votre sécurité et votre confidentialité. Notre système avancé de sécurité des paiements garantit la confidentialité en chiffrant vos informations lors de la transmission grâce aux protocoles AES (Advanced Encryption Standards) et SSL (Secure Socket Layer). Vos coordonnées de paiement sont 100 % sécurisées car nous ne partageons pas vos informations de paiement avec des vendeurs tiers.

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Caractéristiques et avantages

  • 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