摘要
深度学习是机器学习研究中的一个新的领域,深度学习以及神经网络模型是近年来机器学习及人工智能领域新的研究方向及热点问题。其目的是建立、模拟人脑进行分析学习的神经网络,它模仿人脑的机制来进行分析、学习和解释数据,例如图像,声音和文本。深度学习在图像识别应用中已取得了突破性进展,传统的图像识别方法需要人工设计特征,而深度学习属于神经网络结构,它能够从大数据中自动学习特征,极大地提高了识别准确率以及效率。该文介绍了两种深度学习网络模型及其应用,并就讨论内容进行了总结与展望。
Deep learning is a new field in machine learning research.Deep learning and neural network models are new research directions and hot issues in the field of machine learning and artificial intelligence in recent years.Its purpose is to create and simulate a neural network for human brain analysis and learning,which mimics the mechanisms of the human brain to analyze,learn,and interpret data such as images,sounds,and text.Deep learning has made breakthroughs in image recognition applications.Traditional image recognition methods require artificial design features,while deep learning belongs to neural network structure,which can automati cally learn features from big data,greatly improving recognition accuracy.And efficiency.The article introduces two deep learning network models and their applications,and summarizes and forecasts the content of the discussion.
作者
蔡春花
王峰
Cai Chun-hua;Wang Feng(College of information Science and Technology,Henan university of technology,Henan Zhengzhou 450001)
出处
《电子质量》
2018年第9期7-9,12,共4页
Electronics Quality
关键词
深度学习
图像识别
神经网络
deep learning
image recognition
neural network
作者简介
蔡春花（1995-）,女,硕士研究生,研究方向为图像识别、人工智能,E-mail：786803383@qq.com;;通讯作者,王峰（1975-）,男,博士,副教授,研究方向为智能信息处理、模式识别、人工智能等,E-mail：wangfeng_scu@aliyun.com.
