Isbn: 9798269570686 - gpu-powered deep learning: mastering parallel computing for high-performance ai: a practical guide to cuda, optimization, and scalable model deployment (5 risultati)

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Condizione: new. Paperback. GPU-Powered Deep Learning: Unlocking High-Performance AI with Parallel Computing and CUDA is a comprehensive guide for AI engineers, developers, and data scientists seeking to master GPU-accelerated deep learning. Authored by a seasoned AI expert, this book takes you on a journey from the fundamentals of CUDA programming to advanced techniques for scaling and deploying high-performance AI models. Through clear explanations, practical examples, and production-ready code, you'll learn to harness the power of GPUs to build, optimize, and deploy cutting-edge AI systems.The book begins with an introduction to GPU computing and CUDA, providing hands-on skills to write custom kernels and optimize memory usage. It progresses to leveraging high-level frameworks like PyTorch and TensorFlow, exploring optimization techniques such as memory coalescing and mixed-precision training. You'll master multi-GPU and distributed systems, learning to scale models across clusters with tools like Horovod and DALI. Finally, the book covers deploying optimized models for low-latency inference, integrating with modern AI ecosystems like LangChain and vector databases.With real-world case studies from healthcare, finance, and e-commerce, this book bridges theory and practice, offering actionable insights for building scalable, efficient AI solutions. Whether you're training massive language models, deploying real-time vision systems, or integrating with agentic workflows, this book equips you with the expertise to excel in the fast-evolving world of AI. Packed with annotated code, architectural patterns, and ethical considerations, it's an essential resource for professionals aiming to push the boundaries of AI performance. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condizione: New. Print on Demand.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Condizione: new. Paperback. GPU-Powered Deep Learning: Unlocking High-Performance AI with Parallel Computing and CUDA is a comprehensive guide for AI engineers, developers, and data scientists seeking to master GPU-accelerated deep learning. Authored by a seasoned AI expert, this book takes you on a journey from the fundamentals of CUDA programming to advanced techniques for scaling and deploying high-performance AI models. Through clear explanations, practical examples, and production-ready code, you'll learn to harness the power of GPUs to build, optimize, and deploy cutting-edge AI systems.The book begins with an introduction to GPU computing and CUDA, providing hands-on skills to write custom kernels and optimize memory usage. It progresses to leveraging high-level frameworks like PyTorch and TensorFlow, exploring optimization techniques such as memory coalescing and mixed-precision training. You'll master multi-GPU and distributed systems, learning to scale models across clusters with tools like Horovod and DALI. Finally, the book covers deploying optimized models for low-latency inference, integrating with modern AI ecosystems like LangChain and vector databases.With real-world case studies from healthcare, finance, and e-commerce, this book bridges theory and practice, offering actionable insights for building scalable, efficient AI solutions. Whether you're training massive language models, deploying real-time vision systems, or integrating with agentic workflows, this book equips you with the expertise to excel in the fast-evolving world of AI. Packed with annotated code, architectural patterns, and ethical considerations, it's an essential resource for professionals aiming to push the boundaries of AI performance. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…