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Nº de artículo:
142608441
Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python Paperback – 25 Feb. 2022
Nº de artículo:
142608441
CLP 45224
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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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What Stands Out
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.
Detalles de producto
| 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 Origin | This item will be imported from EU |
| What is in the box | Machine Learning with PyTorch... For more details, please check description/product details |
Who Should Buy?
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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.
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Students in ML Courses
Students enrolled in machine learning courses can use this book as a supplemental guide to enhance their understanding.
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Advanced Practitioners
Experienced data scientists may find the content too basic and not challenging enough for advanced learning.
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Non-Programmers
Individuals without programming experience will struggle to grasp key concepts and practical implementations in this book.
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Quick Reference Needs
Those seeking quick, specific solutions may find the book lengthier and detailed than they require for immediate problems.
DESCRIPCIÓN DEL PRODUCTO
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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Preguntas y respuestas de los clientes
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Pregunta:
What are the essential parts of PyTorch?
Respuesta: The book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric. -
Pregunta:
What are the latest trends in deep learning covered in the book?
Respuesta: 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). -
Pregunta:
Who is the book for?
Respuesta: 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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ventajas
- Clear and concise explanations
- Hands-on coding examples
- Covers both ML and DL
- Great for beginners and pros
- Up-to-date with latest tools
Contras
- Could include more advanced topics.
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CLP 45224
Este producto no es suministrado por Ubuy y puede tardar un mínimo de 10 días en ser entregado. Podríamos cancelar el producto del pedido y reembolsarte si surge algún problema con la entrega de este producto.
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Secured transaction
Ubuy works hard to protect your security and privacy. Our advanced payment security system ensures confidentiality by encrypting your information during transmission using AES (Advanced Encryption Standards) and SSL (Secure Socket Layer) protocols. Your payment details are 100% secure as we do not share your payment details with third party sellers.
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características y beneficios
- 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