Andres Rodriguez – författare
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6 produkter
6 produkter
Inbunden, Engelska, 2022
1 002 kr
Skickas inom 5-8 vardagar
The centre may hold, but borders can fray. Frontier Fieldwork explores the work of social scientists, agriculturists, photographers, students, and missionaries who took to the field on China's southwestern border at a time when foreign political powers were contesting China's claims over its frontiers. In the early twentieth century, when the threat of imperialism loomed large in the Sino-Tibetan borderlands, these fieldworkers undertook a nation-building exercise to unite a disparate, multi-ethnic population at the periphery of the country. They saw themselves as a vanguard force, foreshadowing the policies of social development and intervention that would be pursued during the Cold War decades later. Drawing on Chinese and Western materials, Andres Rodriguez exposes the transformative power of the fieldworkers' efforts, which went beyond creating new forms of political action and identity. His incisive study demonstrates that fieldwork placed China's margins at the centre of its nation-making process and race to modernity.
Häftad, Engelska, 2018
240 kr
Skickas inom 3-6 vardagar
22 518 kr
Skickas inom 10-15 vardagar
Geographers (and other social scientists) working in local and regional development ultimately seek to understand how—and to what extent—local and regional policy can positively affect economic growth and progress. This area of research and study throws up difficult and challenging questions, such as: what are the sources of regional development? And how do we account for the uneven development of regions, an apparent feature of geographical development in general, and one which seems to be reinforced by globalization?This new four-volume collection from Routledge meets the need for a comprehensive reference work to allow users to make better sense of the voluminous scholarly and practical literature on these and other issues. Indeed, the sheer scale of the research output—and the breadth of the field—makes this title especially welcome.Local and Regional Development has been assembled by the same editorial team behind Routledge’s award-winning Handbook of Local and Regional Development (2010) (978-0-415-54831-1). The collection is fully indexed and has a comprehensive introduction, newly written by the editors, which places the material in its intellectual context. It is an essential work of reference and is destined to be valued by scholars, advanced students, and policy-makers as a vital one-stop research resource.
Häftad, Galiciska, 2023
397 kr
Skickas inom 5-8 vardagar
Häftad, Engelska, 2020
396 kr
Skickas inom 5-8 vardagar
The book details advancements and adoption of DL models in industry, explains the training and deployment process, describes the essential hardware architectural features needed for today's and future models, and details advances in DL compilers to efficiently execute algorithms across various hardware targets.
E-bok
PDF, Engelska, 2022840 kr
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This book describes deep learning systems: the algorithms, compilers, and processor components to efficiently train and deploy deep learning models for commercial applications. The exponential growth in computational power is slowing at a time when the amount of compute consumed by state-of-the-art deep learning (DL) workloads is rapidly growing. Model size, serving latency, and power constraints are a significant challenge in the deployment of DL models for many applications. Therefore, it is imperative to codesign algorithms, compilers, and hardware to accelerate advances in this field with holistic system-level and algorithm solutions that improve performance, power, and efficiency. Advancing DL systems generally involves three types of engineers: (1) data scientists that utilize and develop DL algorithms in partnership with domain experts, such as medical, economic, or climate scientists; (2) hardware designers that develop specialized hardware to accelerate the components in the DL models; and (3) performance and compiler engineers that optimize software to run more efficiently on a given hardware. Hardware engineers should be aware of the characteristics and components of production and academic models likely to be adopted by industry to guide design decisions impacting future hardware. Data scientists should be aware of deployment platform constraints when designing models. Performance engineers should support optimizations across diverse models, libraries, and hardware targets. The purpose of this book is to provide a solid understanding of (1) the design, training, and applications of DL algorithms in industry; (2) the compiler techniques to map deep learning code to hardware targets; and (3) the critical hardware features that accelerate DL systems. This book aims to facilitate co-innovation for the advancement of DL systems. It is written for engineers working in one or more of these areas who seek to understand the entire system stack in order to bettercollaborate with engineers working in other parts of the system stack. The book details advancements and adoption of DL models in industry, explains the training and deployment process, describes the essential hardware architectural features needed for today''s and future models, and details advances in DL compilers to efficiently execute algorithms across various hardware targets. Unique in this book is the holistic exposition of the entire DL system stack, the emphasis on commercial applications, and the practical techniques to design models and accelerate their performance. The author is fortunate to work with hardware, software, data scientist, and research teams across many high-technology companies with hyperscale data centers. These companies employ many of the examples and methods provided throughout the book.