… The power and capabilities of the deep learning techniques taught in this book have … been
greatly affected by deep learning (DL) and its related supersets of machine learning (ML) and …

… to reframe neural theory as a deep learning problem. … theories and maintain a pointed
focus on explaining computation in biological brains. In other words, we hope that deep learning …

… draws from core theories such as physics, statistics, fusion, and machine learning, to name
… The aim of this paper is to provide resources with respect to theory, tools, and challenges for …

… our work, especially how to extend our result to other types of deep learning tasks and/or
proving generalization. There is already generalization theory (Allen-Zhu et al., 2018a) for over-…

… -theory to adding orientifolds, which we could deep-learn. For now, let us move onto a closely
related subject which has over the last two decades vastly generalized the computation of …

… to know, learn, and apply the relationship between deep learning/machine learning and
physics… Abstract In this chapter, we learn the general theory of machine learning. We shall take a …

… provide a fundamental understanding of deep learning have advanced rapidly in recent …
deep learning theory through the lens of dynamical system and optimal control. By viewing deep …

… become new issues in machinery health monitoring. To harness the properties of … advantages
of deep learning theory, a health monitoring and fault diagnosis method for machinery is …

… Deep learning has been the engine powering many successes of data science. However,
the deep neural network (DNN), as the basic model of deep … ReLU, and our theory allows the …

… In this paper, a novel approach is proposed using graph theory. This … deep learning
convolutional neural networks and the second model leverages a traditional machine learning …