<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Utilising the dispersed resources that are accessible inside a cluster, the dispersed Deep Learning System (DDLS) is able to successfully complete the process of training complex neural network models. This is accomplished by utilising the resources to their full potential. As a consequence of this, the system is able to get insights about neural network models that are becoming more complex. Individuals who work as engineers for distributed deep learning systems are needed to make a variety of decisions in order to properly manage their specialised job within the environment of their choice. This is necessary in order to ensure that the job is efficiently managed. Achieving this is very necessary in order to guarantee that they will be able to carry out their obligations. Throughout the course of the last several years, deep learning programmes have uncovered significant applications in a broad range of different industries. A few examples of these domains include image recognition, natural language processing, semantic understanding, financial analysis, and aided healthcare. These are only few of the topics that fall under this category. All of these factors have led to a significant growth in the amount of information that is being used in each and every application. The continued penetration of information into a number of different sectors, as well as the increasing complexity of computations and the restrictions of models, have all contributed to this. Because of this, there has been a significant increase in the quantity of information that is being employed on a worldwide scale for various purposes.</p>
</abstract>

Utilising the dispersed resources that are accessible inside a cluster, the dispersed Deep Learning System (DDLS) is able to successfully complete the process of training complex neural network models. This is accomplished by utilising the resources to their full potential. As a consequence of this, the system is able to get insights about neural network models that are becoming more complex. Individuals who work as engineers for distributed deep learning systems are needed to make a variety of decisions in order to properly manage their specialised job within the environment of their choice. This is necessary in order to ensure that the job is efficiently managed. Achieving this is very necessary in order to guarantee that they will be able to carry out their obligations. Throughout the course of the last several years, deep learning programmes have uncovered significant applications in a broad range of different industries. A few examples of these domains include image recognition, natural language processing, semantic understanding, financial analysis, and aided healthcare. These are only few of the topics that fall under this category. All of these factors have led to a significant growth in the amount of information that is being used in each and every application. The continued penetration of information into a number of different sectors, as well as the increasing complexity of computations and the restrictions of models, have all contributed to this. Because of this, there has been a significant increase in the quantity of information that is being employed on a worldwide scale for various purposes.

Modular Platforms based on Clouded Web Technology and Distributed Deep Learning Systems

Technical College of Engineering, Duhok Polytechnic University

Journal of Smart Internet of Things

This work is licensed under the Creative Commons Attribution 4.0 International License.

Utilising the dispersed resources that are accessible inside a cluster, the dispersed Deep Learning System (DDLS) is able to successfully complete the process...

Modular Platforms based on Clouded Web Technology and Distributed Deep Learning Systems

Online veröffentlicht: 15. Dez. 2023

Seitenbereich: 154 - 173

Eingereicht: 13. Aug. 2023

Akzeptiert: 21. Sept. 2023

DOI: https://doi.org/10.2478/jsiot-2023-0018

Schlüsselwörter
Modular Platforms, Distributed Systems, Deep learning, Cloud Computing, Web Technology, Machine Learning, Network and GPU

© 2023 Rozin Majeed Abdullah et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Modular Platforms based on Clouded Web Technology and Distributed Deep Learning Systems

Online veröffentlicht: 15. Dez. 2023

Seitenbereich: 154 - 173

Eingereicht: 13. Aug. 2023

Akzeptiert: 21. Sept. 2023

DOI: https://doi.org/10.2478/jsiot-2023-0018

SchlüsselwörterModular Platforms, Distributed Systems, Deep learning, Cloud Computing, Web Technology, Machine Learning, Network and GPU

© 2023 Rozin Majeed Abdullah et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Modular Platforms, Distributed Systems, Deep learning, Cloud Computing, Web Technology, Machine Learning, Network and GPU