深度学习技术浅述

Simple Aclysis of Deep learning Technology

深度学习通过组合低层特征形成更加抽象的高层表示属性类别或特征,以发现数据的分布式特征表示,具有强大的学习和高效的特征表达能力,成为人工智能领域的研究热点,近年来发展十分迅猛。首先介绍了深度学习的特点及其与浅层学习的区别,并详细介绍了深度学习的国内外发展现状和典型的深度学习网络模型,重点介绍了深度学习技术中一些关键环节处理方法。

Deep learning can form high-level representation of attribute categoriesor characteristics through combine low-level features, in order to find the distributed characteristic data, it has the strong learning and efficient feature expression ability, and it becomes a research focus in the field of artificial intelligence and developes very rapidly in recent years. The characteristics of deep learning and its difference from shallow learning are introduced at first. The development status and typical deep learning network models of deep learning are introduced in detail, and finally some key links in deep learning technology are introduced.