Deep neural networks outperform shallow networks in approximating high-dimensional radial functions, revolutionizing data analysis across industries.
Deep convolutional neural networks are a type of deep learning method that can efficiently approximate functions. They are derived from convolutional structures and are particularly good at approximating radial functions in large datasets. These networks perform at least as well as shallow networks and can even outperform them in certain cases.
