Our goal is to provide a review of deep learning methods which provide insight into structured high-dimensional data. Rather than using shallow additive architectures common to most statistical models, deep learning uses layers of semi-affine input transformations to provide a predictive rule. Applying these layers of transformations leads to a set of attributes (or, features) to which probabilistic statistical methods can be applied. Thus, the best of both worlds can be achieved: scalable prediction rules fortified with uncertainty quantification, where sparse regularization finds the features.

翻译：我们的目标是综述深度学习方法，这些方法能够洞察结构化高维数据。与大多数统计模型常用的浅层加性架构不同，深度学习使用半仿射输入变换的层次结构来构建预测规则。应用这些变换层可以得到一组属性（即特征），从而可应用概率统计方法。如此，可以实现两全其美：可扩展的预测规则结合不确定性量化，并通过稀疏正则化发现特征。