Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python
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Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python
Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python
This comprehensive guide covers machine and deep learning using PyTorch's simple to code framework, making it your essential resource for mastering Python machine learning.
Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python
Numéro d'article: 142261462

Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python

87% des répondants recommanderaient ceci à un ami

Numéro d'article: 142261462

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This comprehensive guide covers machine and deep learning using PyTorch's simple to code framework, making it your essential resource for mastering Python machine learning.
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Ce qui se démarque

Comprehensive Coverage
Covers both machine learning and deep learning, providing a holistic understanding of Python applications in AI, ideal for beginners and experienced developers alike.
Hands-On Practice
Includes practical examples and projects, enhancing learning through real-world applications of PyTorch and Scikit-Learn, which aids in skill retention and confidence building.
User-Friendly Approach
Structured to simplify complex concepts, making it accessible for all learners while ensuring depth in knowledge, thus fostering a deeper understanding of advanced techniques.

Détails du produit

Shop Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python online at a best price in Switzerland. 1837021953
  • 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)
Publisher Packt Publishing
Publication date February 25, 2022
Language English
Print length 774 pages
ISBN-10 1837021953
ISBN-13 978-1837021956
Item Weight 3.37 pounds (1.53 kg)
Dimensions 7.5 x 1.63 x 9.25 inches (19.1 x 4.1 x 23.5 cm)

À qui est-ce destiné ?

Suitable For
  • Aspiring Data Scientists

    Ideal for individuals looking to kickstart their careers in data science with hands-on machine learning knowledge.

  • Software Developers

    Great for developers wanting to incorporate machine learning capabilities into applications, enhancing their skill set.

  • Academic Researchers

    Useful for researchers seeking to implement machine learning techniques in their work and projects effectively.

Not Suitable For
  • Absolute Beginners

    Not suitable for those with no prior programming or data analysis experience, as it may be overwhelming.

DESCRIPTION DU PRODUIT

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  • question: Machine Learning with PyTorch and Scikit-Learn: est-il disponible pour effectuer des achats en ligne à Switzerland ?

    répondre: Oui, chez Ubuy Switzerland, ce produit est disponible pour vous à un prix raisonnable.. Le Machine Learning with PyTorch and Scikit-Learn: n'est pas disponible localement mais vous pouvez nous faire confiance avec nos services d'expédition express.
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Expert Systems Editorial Review

**** "Machine Learning with PyTorch and Scikit-Learn" serves as a robust resource for both novice and experienced practitioners in the field of machine learning. The book is organized effectively into two main parts—one covering fundamental machine learning concepts and the other delving into advanced techniques with PyTorch. Readers appreciate the extensive range of topics and the clarity with which complex ideas are communicated, making it a suitable reference for both seasoned professionals and those just starting out. Many users have found the thorough explanations and well-structured code examples particularly beneficial. The author’s ability to break down challenging concepts, such as back-propagation, has been highlighted as a key strength. The practical approach encourages hands-on learning, with readers effectively using Jupyter Notebook to interact with the examples provided. Despite its strengths, some users noted areas where the book could be improved. For instance, while the coverage of algorithms is broad, certain key topics were missing, such as time series forecasting, and some of the code examples relied on older package versions, leading to compatibility issues. Additionally, readers expressed a desire for deeper discussions regarding model architecture and performance optimization to better contextualize the choice of techniques like PyTorch versus simpler methods. Overall, the book is viewed as an invaluable tool for those pursuing machine learning as a lifelong skill, offering both a solid introduction and a comprehensive resource for advancing one's knowledge and coding abilities in a rapidly evolving field. **

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Avantages

  • Extensive coverage from beginner to advanced topics
  • Clear explanations and structured code examples
  • Encourages hands-on interaction and experimentation
  • Suitable for both new learners and experienced ML practitioners
  • Strong grounding in concepts, especially regarding model tracking and tuning

Les inconvénients

  • Does not cover all algorithms, such as time series forecasting

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Caractéristiques et avantages

  • Master machine learning and deep learning with practical applications using PyTorch.
  • Includes clear explanations and visualizations for deeper understanding.
  • Covers modern techniques like GANs, transformers, and graph neural networks.
  • Suitable for both beginners and experienced developers in Python.
  • Features hands-on examples and best practices for real-world applications.
  • Purchase includes a free eBook in PDF format for easy reference.

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