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Heterogeneous Large-Scale Distributed Systems on Machine Learning

Heterogeneous Large-Scale Distributed Systems on Machine Learning

Karthika Paramasivam, Prathap M., Hussain Sharif
Copyright: © 2020 |Pages: 22
ISBN13: 9781799835912|ISBN10: 179983591X|EISBN13: 9781799835929
DOI: 10.4018/978-1-7998-3591-2.ch004
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MLA

Paramasivam, Karthika, et al. "Heterogeneous Large-Scale Distributed Systems on Machine Learning." Deep Neural Networks for Multimodal Imaging and Biomedical Applications, edited by Annamalai Suresh, et al., IGI Global, 2020, pp. 47-68. https://doi.org/10.4018/978-1-7998-3591-2.ch004

APA

Paramasivam, K., M., P., & Sharif, H. (2020). Heterogeneous Large-Scale Distributed Systems on Machine Learning. In A. Suresh, R. Udendhran, & S. Vimal (Eds.), Deep Neural Networks for Multimodal Imaging and Biomedical Applications (pp. 47-68). IGI Global. https://doi.org/10.4018/978-1-7998-3591-2.ch004

Chicago

Paramasivam, Karthika, Prathap M., and Hussain Sharif. "Heterogeneous Large-Scale Distributed Systems on Machine Learning." In Deep Neural Networks for Multimodal Imaging and Biomedical Applications, edited by Annamalai Suresh, R. Udendhran, and S. Vimal, 47-68. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-3591-2.ch004

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Abstract

Tensor flow is an interface for communicating AI calculations and a use for performing calculations like this. A calculation communicated using tensor flow can be done with virtually zero changes in a wide range of heterogeneous frameworks, ranging from cell phones, for example, telephones and tablets to massive scale-appropriate structures of many computers and a large number of computational gadgets, for example, GPU cards. The framework is adaptable and can be used to communicate a wide range of calculations, including the preparation and derivation of calculations for deep neural network models, and has been used to guide the analysis and send AI frameworks to more than twelve software engineering zones and different fields, including discourse recognition, sight of PCs, electronic technology, data recovery, everyday language handling, retrieval of spatial data, and discovery of device medication. This chapter demonstrates the tensor flow interface and the interface we worked with at Google.

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