These lectures, presented at the 2022 Les Houches Summer School on Statistical Physics and Machine Learning, focus on the infinite-width limit and large-width regime of deep neural networks. Topics covered include various statistical and dynamical properties of these networks. In particular, the lecturers discuss properties of random deep neural networks; connections between trained deep neural networks, linear models, kernels, and Gaussian processes that arise in the infinite-width limit; and perturbative and non-perturbative treatments of large but finite-width networks, at initialization and after training.

翻译：这些讲座于2022年莱斯胡斯统计物理与机器学习暑期学校讲授，聚焦深度神经网络的无限宽度极限与大宽度机制。涵盖内容包括这些网络的各种统计与动力学性质。具体而言，讲座探讨了随机深度神经网络的特性；训练后的深度神经网络与在无限宽度极限下出现的线性模型、核函数及高斯过程之间的关联；以及针对初始化和训练后有限但大宽度网络的微扰与非微扰处理方法。