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
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
Numéro d'article: 45820631

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

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Numéro d'article: 45820631

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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 Coverage
This product provides an in-depth exploration of both machine learning and deep learning, ensuring users can master a wide range of algorithms and techniques using two of the most powerful Python libraries.
Practical Applications
Focuses on practical implementations, enabling users to apply theoretical knowledge directly to real-world problems, enhancing their skills and confidence in developing robust models.
User-Friendly Learning
Designed for all skill levels, the content is accessible and well-structured, allowing beginners to learn foundational concepts while also offering advanced insights for experienced developers.

Détails du produit

Get started with machine learning and deep learning using PyTorch and Scikit-Learn. Develop powerful models in Python. Explore Ubuy Belgium's wide selection now.
  • 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 770 pages
ISBN-10 1801819319
ISBN-13 978-1801819312
Dimensions 7.5 x 1.75 x 9.25 inches (19.1 x 4.4 x 23.5 cm)
Package Weight4.34 Pound

À qui est-ce destiné ?

Suitable For
  • Beginners in ML

    Great for those starting out in machine learning, providing foundational concepts and hands-on experience with Python.

  • Data Scientists

    Ideal for data scientists looking to enhance their skills in deep learning using popular libraries like PyTorch.

  • Python Developers

    Helpful for Python developers wanting to get into machine learning and leverage their programming skills effectively.

Not Suitable For
  • Advanced Experts

    Not suitable for experts seeking advanced deep learning techniques or cutting-edge research applications.

DESCRIPTION DU PRODUIT

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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Questions et réponses des clients

  • question: What are the prerequisites for learning Machine Learning with PyTorch and Scikit-Learn?

    répondre: To effectively learn Machine Learning with PyTorch and Scikit-Learn, it's essential to have a basic understanding of Python programming, mathematics (particularly linear algebra and statistics), and familiarity with fundamental machine learning concepts. These foundations will help you grasp the algorithms and their implementations more easily. For example, understanding Python will allow you to navigate libraries easily, while math skills will aid in comprehending model performance and tuning.
  • question: How can I implement deep learning using PyTorch?

    répondre: Implementing deep learning with PyTorch involves defining a model architecture, selecting appropriate loss functions, and employing optimizers for training. PyTorch provides a flexible interface to build neural networks using its 'torch.nn' module. You can start by creating a simple feedforward neural network and gradually move to more complex architectures like CNNs or RNNs. For instance, it’s common to use convolutional neural networks for image recognition tasks due to their effective feature extraction capabilities.
  • question: What are the key differences between PyTorch and Scikit-Learn?

    répondre: PyTorch is primarily designed for deep learning and provides robust flexibility for building complex models, while Scikit-Learn is optimized for traditional machine learning algorithms. PyTorch excels in tasks requiring large datasets and high-level abstractions like neural networks, making it suitable for advanced applications. In contrast, Scikit-Learn is more straightforward for regression, classification, and clustering tasks. Depending on your project, you might first prototype with Scikit-Learn and later implement deep learning models in PyTorch.
  • question: Can I use PyTorch with GPU acceleration?

    répondre: Yes, PyTorch natively supports GPU acceleration, which drastically improves performance for deep learning tasks. By leveraging CUDA-enabled GPUs, you can significantly speed up model training and inference. This is particularly beneficial for large-scale datasets and complex models where computation can be intensive. In practical terms, this means that instead of running your machine learning algorithms solely on a CPU, you can take advantage of the parallel processing capabilities of GPUs.
  • question: What kinds of projects can I build with Machine Learning using these tools?

    répondre: With PyTorch and Scikit-Learn, you can develop a wide array of projects, from simple regression models predicting housing prices to advanced image classification systems. For example, you can create a sentiment analysis tool that classifies text based on social media data or build a recommendation system for e-commerce platforms. These applications not only demonstrate your skills but also provide valuable insights into real-world data.
  • question: Is there a community available for support while learning these tools?

    répondre: Absolutely! Both PyTorch and Scikit-Learn have strong communities offering support through forums, online courses, and extensive documentation. Platforms like GitHub provide repositories with examples and collaborative projects which can enhance your learning experience. Engaging in these communities can also help you stay updated on best practices and new advancements in machine learning. Joining forums like Stack Overflow also provides quick answers to common questions and issues.
  • question: What role does data preprocessing play in machine learning?

    répondre: Data preprocessing is a crucial step in machine learning that involves cleaning, transforming, and preparing data for modeling. Effective preprocessing can significantly enhance model performance. Common tasks include handling missing values, normalizing data, and encoding categorical variables. By ensuring your data is well-prepared, you can improve the accuracy and reliability of your machine learning models. For instance, unnormalized data can lead to models that fail to converge or perform poorly.
  • question: How does model evaluation work in machine learning?

    répondre: Model evaluation is the process of assessing how well your machine learning model performs using specific metrics. Common evaluation techniques involve splitting your data into training and test sets and using metrics like accuracy, precision, recall, and F1 score. This allows you to gauge the model’s effectiveness and make necessary adjustments. Evaluating models helps to fine-tune parameters and choose the best algorithm for your data, ensuring better predictions in real applications.
  • question: Are there any certifications available for Machine Learning with these tools?

    répondre: Yes, there are various online platforms offering certifications in Machine Learning using PyTorch and Scikit-Learn. These certifications often cover essential topics and provide hands-on experience with projects. Obtaining such credentials can enhance your resume and validate your skills to potential employers. Platforms like Coursera and edX host courses designed by recognized institutions, often culminating in a certification that showcases your knowledge and capabilities in the field.
  • question: Where can I buy Machine Learning with PyTorch and Scikit-Learn?

    répondre: You can purchase 'Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python' on Ubuy in Belgium. Ubuy provides access to a wide range of titles and often features user reviews and ratings, helping you make an informed decision before purchasing. It’s a reliable platform to explore not just this book, but also various educational resources on machine learning.

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"Machine Learning with PyTorch and Scikit-Learn" is an excellent textbook for both beginners and experienced practitioners of machine learning. The book covers a wide range of useful terms in the machine learning landscape and provides practical code examples, a detailed explanation of how various library tools work, and exposure to the mathematical concepts behind machine learning algorithms. The authors have managed to explain complex mathematical operations in simple terms that are easy to follow. The book seamlessly connects all the chapters, making it easy for readers to understand how each algorithm works and how to avoid mistakes. The authors have also broken down complex mathematical operations and explained them in simple terms. The book introduces readers to PyTorch, exposing them to all the sophisticated libraries that help them learn how to maximize their computing power. It is not intimidating, even for those who have not used PyTorch before. The book is a bargain, and readers get value for their money. The authors provide an excellent presentation of the material, making it suitable for use as a textbook for studying machine learning comprehensively or as a reference for experienced practitioners. The book covers more than 700 pages, with a wide range of topics that will help beginners and experienced practitioners alike. The book is easy to read and modern in its content, making it a 10/10 for anyone seeking a textbook that applies well to both beginners and experienced practitioners.

Avis et évaluations clients

526 évaluations des clients
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  • 4 étoile
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Avantages

  • Comprehensive coverage of machine learning concepts
  • Practical code examples
  • Detailed explanation of various library tools
  • Exposure to mathematical concepts behind machine learning algorithms
  • Intuitively explains how each algorithm works
  • Easy to follow
  • Seamless connection between chapters
  • Includes external resources to spark curiosity
  • Excellent presentation of material
  • Suitable for use as a textbook or reference
  • Easy to read and modern

Les inconvénients

  • Black and white pages

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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

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