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Machine Learning with PyTorch and Scikit-Learn: A Comprehensive Guide to Modern AI Development and Production Deployment
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- FormatePub
- ISBN8231844593
- EAN9798231844593
- Date de parution23/05/2025
- Protection num.pas de protection
- Infos supplémentairesepub
- ÉditeurWalzone Press
Résumé
Machine Learning with PyTorch and Scikit-Learn: A Comprehensive Guide to Modern AI Development and Production Deployment is the definitive resource for mastering machine learning and deep learning with Python's most powerful frameworks. This comprehensive guide takes you from foundational concepts to advanced production deployment, covering both classical machine learning with Scikit-Learn and cutting-edge deep learning with PyTorch.
The book features 14 detailed chapters with over 100 practical sections, covering everything from data preprocessing and neural networks to transformers, generative models, and MLOps. You'll learn to build real-world applications including computer vision systems, natural language processing models, recommendation engines, and autonomous systems. Advanced topics include distributed training, model optimization, federated learning, and emerging technologies like quantum machine learning.
Written for developers and data scientists with Python experience, this book emphasizes practical implementation alongside theoretical understanding. Each chapter includes hands-on projects, code examples, and best practices for production deployment. Whether you're building your first neural network or scaling models for enterprise applications, this guide provides the knowledge and tools needed to succeed in today's AI-driven landscape.
The book bridges the gap between academic concepts and industry applications, making it essential for anyone serious about machine learning development.
The book features 14 detailed chapters with over 100 practical sections, covering everything from data preprocessing and neural networks to transformers, generative models, and MLOps. You'll learn to build real-world applications including computer vision systems, natural language processing models, recommendation engines, and autonomous systems. Advanced topics include distributed training, model optimization, federated learning, and emerging technologies like quantum machine learning.
Written for developers and data scientists with Python experience, this book emphasizes practical implementation alongside theoretical understanding. Each chapter includes hands-on projects, code examples, and best practices for production deployment. Whether you're building your first neural network or scaling models for enterprise applications, this guide provides the knowledge and tools needed to succeed in today's AI-driven landscape.
The book bridges the gap between academic concepts and industry applications, making it essential for anyone serious about machine learning development.
Machine Learning with PyTorch and Scikit-Learn: A Comprehensive Guide to Modern AI Development and Production Deployment is the definitive resource for mastering machine learning and deep learning with Python's most powerful frameworks. This comprehensive guide takes you from foundational concepts to advanced production deployment, covering both classical machine learning with Scikit-Learn and cutting-edge deep learning with PyTorch.
The book features 14 detailed chapters with over 100 practical sections, covering everything from data preprocessing and neural networks to transformers, generative models, and MLOps. You'll learn to build real-world applications including computer vision systems, natural language processing models, recommendation engines, and autonomous systems. Advanced topics include distributed training, model optimization, federated learning, and emerging technologies like quantum machine learning.
Written for developers and data scientists with Python experience, this book emphasizes practical implementation alongside theoretical understanding. Each chapter includes hands-on projects, code examples, and best practices for production deployment. Whether you're building your first neural network or scaling models for enterprise applications, this guide provides the knowledge and tools needed to succeed in today's AI-driven landscape.
The book bridges the gap between academic concepts and industry applications, making it essential for anyone serious about machine learning development.
The book features 14 detailed chapters with over 100 practical sections, covering everything from data preprocessing and neural networks to transformers, generative models, and MLOps. You'll learn to build real-world applications including computer vision systems, natural language processing models, recommendation engines, and autonomous systems. Advanced topics include distributed training, model optimization, federated learning, and emerging technologies like quantum machine learning.
Written for developers and data scientists with Python experience, this book emphasizes practical implementation alongside theoretical understanding. Each chapter includes hands-on projects, code examples, and best practices for production deployment. Whether you're building your first neural network or scaling models for enterprise applications, this guide provides the knowledge and tools needed to succeed in today's AI-driven landscape.
The book bridges the gap between academic concepts and industry applications, making it essential for anyone serious about machine learning development.