Research Progress and Development of Deep Learning Based on Convolutional Neural Network

Hao Tang
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引用次数: 1

Abstract

As a popular branch of the field of artificial intelligence and machine learning, deep learning has received increasing attention and continuous development. This paper first introduces the development and current situation of deep learning. Then, it introduces the component and mathematical theories of convolutional neural network (CNN). As for CNN optional variables and parameters, the optimal range of each parameter tested is explored through the training and tests on the datasets of Fashion-MNIST and CIFAR20 respectively. Finally, this paper proposes existing defects and future development of deep learning based on CNN.
基于卷积神经网络的深度学习研究进展
作为人工智能和机器学习领域的一个热门分支,深度学习得到了越来越多的关注和不断的发展。本文首先介绍了深度学习的发展和现状。然后介绍了卷积神经网络(CNN)的组成和数学原理。对于CNN可选变量和参数,分别通过Fashion-MNIST和CIFAR20数据集上的训练和测试,探索每个被测参数的最优范围。最后,提出了基于CNN的深度学习存在的缺陷和未来的发展方向。
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