首页|基于卷积神经网络探讨深度学习算法与应用

基于卷积神经网络探讨深度学习算法与应用

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近些年来,深度学习得到了广泛的关注,已经成为计算机科学机器学习重要的领域和方向,深度学习已经被引入到机器学习中,进而与人工智能这一最初的目标更为接近。深度学习包括学习样本数据在内,是一种表示层次和内在规律。深度学习对于解释声音数据、图像数据、文字数据等帮助很大。使机器可以像人类一样,具有很强的分析学习能力,这便是深度学习的目标。通过深度学习,机器可以对声音、图像以及文字等数据进行有效识别。该文中,笔者就基于卷积神经网络探讨深度学习算法与应用。
Depth Learning Algorithm and Application based on Convolutional Neural Network
in recent years, the depth of learning has been widely concerned, has already become the important research field and direction of the computer science and machine learning, depth of learning has been introduced into the machine learning, and artifi?cial intelligence that initial goal closer. Deep learning, including learning sample data, is a kind of expression levels and inherent laws. Depth of learning for interpretation of sound data, image data, text data and other help. The machine can be like human be?ings, with a strong analysis of learning ability, this is the goal of deep learning. Through the depth of learning, the machine can ef?fectively identify data such as sound, image and text.. In this paper, the author discusses the depth based learning algorithm and ap?plication based on convolutional neural network.

depth learningconvolution neural networkpattern recognitionalgorithmapplication

高强、靳其兵、程勇

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北京化工大学信息科学与技术学院,北京100029

深度学习 卷积神经网络 模式识别 算法 应用

2015

电脑知识与技术
时代出版传媒股份有限公司 中国计算机函授学院

电脑知识与技术

影响因子:0.297
ISSN:1009-3044
年,卷(期):2015.(13)
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