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SKU/Artículo: AMZ-B0GD19LM4C

High-Performance Deep Learning with Apache MXNet: Designing, Training, and Deploying Neural Networks at Scale

Format:

Kindle

Hardcover

Kindle

Paperback

Detalles del producto

Disponibilidad:
Fuera de stock

Peso con empaque:
0.36 kg

Devolución:
No

Condición
Nuevo

Producto de:
Amazon

Viaja desde
USA

Sobre este producto

High-Performance Deep Learning with Apache MXNet is a practical, in-depth guide for building, optimizing, and deploying scalable neural networks in real-world environments. It bridges the gap between research prototypes and production-grade deep learning systems by showing how to fully leverage the performance capabilities of Apache MXNet.The book moves beyond basic tutorials to explain how MXNet works internally, covering core components such as NDArray, Gluon, hybridization, operator fusion, and distributed execution. Readers learn how to design efficient architectures, optimize training for single and multiple GPUs, and reduce latency in inference pipelines. Each concept is grounded in real performance challenges commonly encountered when models are deployed at scale.Special attention is given to high-throughput data pipelines, custom training loops, memory management, mixed-precision training, and techniques for preventing GPU starvation. These topics are essential for maximizing hardware utilization and achieving consistent performance in production workloads. Practical examples demonstrate how to apply these strategies across domains including computer vision, natural language processing, and time-series modeling.The book also explores scalable deployment strategies, use of pretrained models, and multi-node distributed training, enabling readers to transition confidently from experimentation to enterprise-level systems. Whether working on edge devices, cloud platforms, or large GPU clusters, readers gain the skills needed to build robust, high-performance AI solutions.Designed for engineers, researchers, and practitioners, this book equips readers with the technical depth, optimization techniques, and architectural insights required to design, train, and deploy deep learning models efficiently. By the end, readers will not only understand Apache MXNet—they will know how to use it to build faster, smarter, and more scalable AI systems.

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