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<p>Deep Convolution Neural Networks (DCNNs), used in Artificial-Intelligence and Machine-Learning applications, require the use of advanced computing platforms. The procesing, exploriation, and storage of large sets of complex data is central to modern business operations and has ability to the rise of Big Data. Distributed frameworks offer efficient methods for the significantly speedy analysis of big data. The use of DCNNs in deep learning makes it possible to work with large complex datasets because it greatly increases the speed of training and inference. Understanding the usage of district frameworks for deep learning convoluted neural networks (DCNN) is crucial for advancing the field of applied deep learning. In seeking to improve performance DCNN's scalability, efficiency, and resource consumption many applications of deep neural networks are seeking out the best distributed computing frameworks, which are usually compared against Hadoop and Spark technologies. The use of currently available distributed frameworks for deep learning DCNN models that have the best performance when working with big data sets is what this article analyzes and compares.</p>

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Performance Insights of Convolutional Neural Networks Operating on Distributed Computing Platforms

  • 期刊论文

  • 发表日期：

    2025年4月10日

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  • Preeti Chaudhary,
  • Satvik Vats,
  • Vikrant Sharma

摘要

Deep Convolution Neural Networks (DCNNs), used in Artificial-Intelligence and Machine-Learning applications, require the use of advanced computing platforms. The procesing, exploriation, and storage of large sets of complex data is central to modern business operations and has ability to the rise of Big Data. Distributed frameworks offer efficient methods for the significantly speedy analysis of big data. The use of DCNNs in deep learning makes it possible to work with large complex datasets because it greatly increases the speed of training and inference. Understanding the usage of district frameworks for deep learning convoluted neural networks (DCNN) is crucial for advancing the field of applied deep learning. In seeking to improve performance DCNN's scalability, efficiency, and resource consumption many applications of deep neural networks are seeking out the best distributed computing frameworks, which are usually compared against Hadoop and Spark technologies. The use of currently available distributed frameworks for deep learning DCNN models that have the best performance when working with big data sets is what this article analyzes and compares.