学术报告：Neural Networks and Deep Learning

报告专家：王玉龙

报告时间：2017年2月27日（星期一）9:00-10:30

报告地点：数统学院511报告厅

专家简介：王玉龙，博士毕业于澳门大学计算机和信息科学系，主要从事模式识别与机器学习研究。2010年和2013年在湖北大学获得学士学位和硕士学位。目前，在模式识别与机器学习领域已发表了10余篇SCI论文，其中包括8篇国际权威期刊IEEE Transactions论文，如IEEE Transactions on Image Processing, IEEE Transactions on Signal Processing，IEEE Transactions on Neural Networks and Learning Systems等。

报告摘要：Recently, deep learning has achieved great success in a wide range of areas, such as speech recognition, image recognition, and natural language processing. Artificial neural network is a biologically-inspired programming paradigm which enables a computer to learn from observational data. Deep learning methods, also called deep neural networks, are representation learning with multiple levels of representation, obtained by composing simple but non-linear modules that each transforms the representation at one level into a representation at a higher, slightly more abstract level. This report aims to introduce the history and some basic facts of neural networks and deep learning from a mathematical prospective.