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Review of Deep Learning Parallelization and Its Application in Spatial Data Mining

机译：深度学习并行化及其在空间数据挖掘中的应用综述

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摘要

In recent years, deep learning has achieved great success in the field of artificial intelligence. The rise of deep learning has accelerated the advent of the intelligent era. However, with the deepening of the layers of deep neural networks and the increasing complexity of algorithm models, the size of the data set used for training is also getting larger and larger, which causes the time required for training to continue to increase, and the parallelization of deep neural networks can effectively solve this problem. Therefore, the research on the parallelization of deep learning becomes more and more important. This article summarizes the current research status of deep learning and its parallelization implementation, and also introduces the related research progress in spatial data processing especially.
机译：近年来，深入学习在人工智能领域取得了巨大成功。深度学习的兴起加速了智能时代的出现。然而，随着深度神经网络的层的深化和算法模型的增加，用于训练的数据集的大小也变大，更大，这导致培训继续增加所需的时间，以及深神经网络的并行化可以有效地解决这个问题。因此，对深度学习的并行化的研究变得越来越重要。本文总结了深度学习的当前研究现状及其并行化实施，特别是特别是在空间数据处理中介绍了相关的研究进展。

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