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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 is a comprehensive guide to machine learning and deep learning with PyTorch.
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- 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 | 25 Feb. 2022 |
| Language | English |
| Print length | 770 pages |
| ISBN-10 | 1801819319 |
| ISBN-13 | 978-1801819312 |
| Item weight | 1.4 kg |
| Dimensions | 19.05 x 4.45 x 23.5 cm |
Quem Deverá Comprar?
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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.
DESCRIÇÃO DO PRODUTO
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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Higher Education Editorial Review
Machine Learning With PyTorch And Scikit-Learn Develop Machine Learning And Deep Learning Models With Python is a comprehensive guide designed for experienced developers and machine learning enthusiasts. This book covers a vast array of topics from the basics to nuanced depths including training sets, dimension reduction, ensemble methods, and advanced concepts like transformers and GANs. As noted in the reviews, it's packed with over 700 pages of in-depth content that serves well as a reference, highlighting the practical use of Scikit-Learn together with PyTorch to ease the learning curve for many. Readers appreciate that the code samples provided are actionable and effective, reinforcing the concepts discussed throughout the text. Furthermore, the breakdown of complex topics into manageable sections makes it ideal for those looking to deepen their understanding without feeling overwhelmed. Ideal for anyone wanting to navigate the expansive world of machine learning.
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Prós
- Comprehensive coverage of machine learning topics
- Suitable for experienced developers
- Hands-on coding samples included
- Suitable as a reference guide
- In-depth explanations of advanced techniques
Contras
- May be overwhelming due to vast content coverage
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Recursos e benefícios
- 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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