关于深度学习的综述与讨论

机器学习是通过计算模型和算法从数据中学习规律的一门学问，在各种需要从复杂数据中挖掘规律的领域中有很多应用，已成为当今广义的人工智能领域最核心的技术之一。近年来，多种深度神经网络在大量机器学习问题上取得了令人瞩目的成果，形成了机器学习领域最亮眼的一个新分支――深度学习，也掀起了机器学习理论、方法和应用研究的一个新高潮。对深度学习代表性方法的核心原理和典型优化算法进行了综述，回顾与讨论了深度学习与以往机器学习方法之间的联系与区别，并对深度学习中一些需要进一步研究的问题进行了初步讨论。
Machine learning is a discipline that involves learning rules from data with mathematical models and computer algorithms. It is becoming one of the core technologies in the field of artificial intelligence, and it is useful for many applications that require mining rules from complex data. In recent years, various deep neural network models have achieved remarkable results in many fields, and this has given rise to an interesting new branch of the machine learning:deep learning. Deep learning leads the new wave of studies on theories, methods, and applications of machine learning. This article reviews the relationships and differences between deep learning and previous machine learning methods, summarizes the key principles and typical optimization algorithms of representative deep learning methods, and discusses some remaining problems that need to be further addressed