PyTorch Foundations and Applications (eBook, ePUB)
Definitive Reference for Developers and Engineers
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"PyTorch Foundations and Applications" "PyTorch Foundations and Applications" is a comprehensive and meticulously structured guide designed for practitioners and researchers aiming to master modern deep learning workflows with PyTorch. Through a clear progression-from fundamental concepts to advanced deployment scenarios-this book covers the inner workings of the PyTorch framework, including its core architecture, tensor operations, memory management, and automatic differentiation system. Early chapters equip readers with the practical skills needed for efficient data handling, robust model co...
"PyTorch Foundations and Applications"
"PyTorch Foundations and Applications" is a comprehensive and meticulously structured guide designed for practitioners and researchers aiming to master modern deep learning workflows with PyTorch. Through a clear progression-from fundamental concepts to advanced deployment scenarios-this book covers the inner workings of the PyTorch framework, including its core architecture, tensor operations, memory management, and automatic differentiation system. Early chapters equip readers with the practical skills needed for efficient data handling, robust model composition, and the management of computing resources across CPUs, GPUs, and distributed environments.
Moving beyond the basics, the book delves deep into the mechanisms of model training, optimization, and advanced techniques such as custom autograd functions, quantization, and model compression. Readers are introduced to hands-on strategies for implementing scalable, high-performance training pipelines, leveraging mixed precision, and deploying models in production settings using robust MLOps practices. Each topic is illustrated with best practices for testing, debugging, reproducibility, and compliance with security and privacy standards-ensuring that models are not only performant but also reliable and secure for real-world applications.
The final sections illustrate PyTorch's versatility across diverse application domains, from computer vision and natural language processing to audio processing, reinforcement learning, and geometric deep learning. The book concludes with an exploration of the PyTorch ecosystem, community engagement, and emerging trends such as PyTorch 2.0 and compiler-based optimizations. Rich with references to domain-specific tools and ongoing research integration, "PyTorch Foundations and Applications" serves both as an authoritative reference and a practical handbook, empowering readers to build, scale, and maintain state-of-the-art machine learning solutions with confidence.
"PyTorch Foundations and Applications" is a comprehensive and meticulously structured guide designed for practitioners and researchers aiming to master modern deep learning workflows with PyTorch. Through a clear progression-from fundamental concepts to advanced deployment scenarios-this book covers the inner workings of the PyTorch framework, including its core architecture, tensor operations, memory management, and automatic differentiation system. Early chapters equip readers with the practical skills needed for efficient data handling, robust model composition, and the management of computing resources across CPUs, GPUs, and distributed environments.
Moving beyond the basics, the book delves deep into the mechanisms of model training, optimization, and advanced techniques such as custom autograd functions, quantization, and model compression. Readers are introduced to hands-on strategies for implementing scalable, high-performance training pipelines, leveraging mixed precision, and deploying models in production settings using robust MLOps practices. Each topic is illustrated with best practices for testing, debugging, reproducibility, and compliance with security and privacy standards-ensuring that models are not only performant but also reliable and secure for real-world applications.
The final sections illustrate PyTorch's versatility across diverse application domains, from computer vision and natural language processing to audio processing, reinforcement learning, and geometric deep learning. The book concludes with an exploration of the PyTorch ecosystem, community engagement, and emerging trends such as PyTorch 2.0 and compiler-based optimizations. Rich with references to domain-specific tools and ongoing research integration, "PyTorch Foundations and Applications" serves both as an authoritative reference and a practical handbook, empowering readers to build, scale, and maintain state-of-the-art machine learning solutions with confidence.
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