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Future of Information and Communication Conference

FICC 2019: Advances in Information and Communication pp 118–130Cite as

Comparing TensorFlow Deep Learning Performance and Experiences Using CPUs via Local PCs and Cloud Solutions

Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 70)

Abstract

Deep Learning is a multilevel learning tool that helps better understand information from various sources. For those who are newly entering the Deep Learning domain, the use of TensorFlow is an optimal way to enhance and learn about neural network designs and a way to deep dive into machine learning. Even though TensorFlow is early with its inception, it is however quickly becoming the direction in which many researchers are using for their open source machine-learning framework. The research, we take advantage of, is the capabilities of TensorFlow to enhance the processing of a substantial dataset that shows the growth and accuracy of cells of embryos in incubation. A typical approach to execute such a massive dataset, today’s researchers would first go to directly to the cloud to accomplish this output. From the experiments that ran in this paper, we concluded that an individual afresh to Deep Learning will not always need to proceed to a large GPU cloud solution setup for optional results. The research presented, garners the usage of local PC CPUs, in which we conclude, introduce a beginner to deep learning, less frustration, easier setup time, and better cost initiatives, than one would get if adopting a cloud solution.

Keywords

  • Machine learning frameworks
  • Convolutional neural network (CNN)
  • Deep learning
  • Artificial intelligence

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Author information

Authors and Affiliations

  1. Pace University, Pleasantville, NY, 10570, USA

    Robert Nardelli & Zachary Dall

  2. Pace University, New York, NY, 10038, USA

    Sotiris Skevoulis

Authors
  1. Robert Nardelli
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  2. Zachary Dall
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  3. Sotiris Skevoulis
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Corresponding author

Correspondence to Robert Nardelli .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Prof. Dr. Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, UK

    Rahul Bhatia

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Nardelli, R., Dall, Z., Skevoulis, S. (2020). Comparing TensorFlow Deep Learning Performance and Experiences Using CPUs via Local PCs and Cloud Solutions. In: Arai, K., Bhatia, R. (eds) Advances in Information and Communication. FICC 2019. Lecture Notes in Networks and Systems, vol 70. Springer, Cham. https://doi.org/10.1007/978-3-030-12385-7_10

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