Why Rectified Linear Unit Is Efficient in Machine Learning: One More Explanation
摘要
In many applications, in particular, in econometric application, deep learning techniques are very effective. In this paper, we provide a new explanation for why rectified linear units—the main units of deep learning—are so effective. This explanation is similar to the usual explanation of why Gaussian (normal) distributions are ubiquitous—namely, it is based on an appropriate limit theorem.