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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
Vare #: 142608441

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

Vare #: 142608441

DKK 315

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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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Hvad der skiller sig ud

Comprehensive Coverage
Offers detailed insights into both machine learning and deep learning frameworks, enhancing the reader's ability to tackle a broader range of data science projects competently.
Practical Examples
Includes hands-on projects throughout, allowing readers to apply theoretical knowledge in real-world scenarios, bolstering skill retention and practical application.
Beginner-Friendly
Written for all skill levels, the book simplifies complex concepts, making cutting-edge technologies accessible to novices and experienced developers alike.

Produktdetaljer

Shop Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python Paperback – 25 Feb. 2022 online at a best price in Faroe Islands. 1801819319
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 OriginThis item will be imported from EU
What is in the boxMachine Learning with PyTorch... For more details, please check description/product details

Hvem bør købe?

Suitable For
  • Aspiring Data Scientists

    Ideal for beginners looking to gain practical skills in machine learning and deep learning with hands-on projects.

  • Developers Familiar with Python

    Python developers wanting to integrate machine learning into applications will find this resource invaluable for skill enhancement.

  • Students in ML Courses

    Students enrolled in machine learning courses can use this book as a supplemental guide to enhance their understanding.

Not Suitable For
  • Advanced Practitioners

    Experienced data scientists may find the content too basic and not challenging enough for advanced learning.

  • Non-Programmers

    Individuals without programming experience will struggle to grasp key concepts and practical implementations in this book.

  • Quick Reference Needs

    Those seeking quick, specific solutions may find the book lengthier and detailed than they require for immediate problems.

PRODUKTBESKRIVELSE

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.

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Kundespørgsmal & svar

  • spørgsmål: What are the essential parts of PyTorch?

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

    svar: 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).
  • spørgsmål: Who is the book for?

    svar: This book is for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch.

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Del dine tanker med andre kunder

Fordele

  • Clear and concise explanations
  • Hands-on coding examples
  • Covers both ML and DL
  • Great for beginners and pros
  • Up-to-date with latest tools

Ulemper

  • Could include more advanced topics.

Produktets prishistorik

Vigtig information

  • Begrænsninger : ved produkter som forsendes internationalt skal du venligst være opmærksom på, at eventuelle garantier fra producenten ikke nødvendigvis gælder; producentens tjenester vil måske ikke være tilgængelige; produktmanualer, instruktioner og sikkerhedsoplysninger vil måske ikke findes på destinationslandets sprog. Produkterne samt tilhørende materialer vil måske ikke være designet i overensstemmelse med destinationslandets nationale standarder, specifikationer og krav til mærkning; og produkterne lever ikke nødvendigvis op til destinationslandets standarder for spænding og strøm (hvilket betyder, at en adapter eller omformer kan være nødvendig). Køber er ansvarlig for at sikre, at produktet kan importeres på lovlig vis til destinationslandet. Ved køb hos Ubuy eller Ubuys filialer er køberen officiel importøt og skal følge alle destinationslandets love og regler.
  • Det er ikke alle de produkter som vises på Ubuy som er til salg, idet Ubuy er en global søgemaskine. Produkterne er underlagt eksport-/handelsregler.

DKK 315

Dette produkt er ikke færdigbehandlet af Ubuy og kan tage mindst 10 dage at levere. Vi annullerer eventuelt produktet fra ordren og tilbagebetaler købet, hvis der opstår problemer med leveringen af dette produkt.

QTY:

Sikker transaktionSikker transaktion

Ubuy arbejder hårdt for at beskytte din sikkerhed og dit privatliv. Vores avancerede betalingssikkerhedssystem sikrer fortrolighed ved at kryptere dine oplysninger under transmission ved hjælp af AES (Advanced Encryption Standards) og SSL (Secure Socket Layer) protokoller. Dine betalingsoplysninger er 100% sikre, da vi ikke deler dine betalingsoplysninger med tredjepartssælgere.

Vores bedste logistikpartnere
  • fedex
  • dhl
Hurtigste grænseoverskridende levering

Egenskaber og fordele

  • 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