This tutorial–review on applications of artificial neural networks in photonics targets a broad audience, ranging from optical research and engineering communities to computer science and applied mathematics. We focus here on the research areas at the interface between these disciplines, attempting to find the right balance between technical details specific to each domain and overall clarity. First, we briefly recall key properties and peculiarities of some core neural network types, which we believe are the most relevant to photonics, also linking the layer’s theoretical design to some photonics hardware realizations. After that, we elucidate the question of how to fine-tune the selected model’s design to perform the required task with optimized accuracy. Then, in the review part, we discuss recent developments and progress for several selected applications of neural networks in photonics, including multiple aspects relevant to optical communications, imaging, sensing, and the design of new materials and lasers. In the following section, we put a special emphasis on how to accurately evaluate the complexity of neural networks in the context of the transition from algorithms to hardware implementation. The introduced complexity characteristics are used to analyze the applications of neural networks in optical communications, as a specific, albeit highly important example, comparing those with some benchmark signal-processing methods. We combine the description of the well-known model compression strategies used in machine learning, with some novel techniques introduced recently in optical applications of neural networks. It is important to stress that although our focus in this tutorial–review is on photonics, we believe that the methods and techniques presented here can be handy in a much wider range of scientific and engineering applications.

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用于光子应用的人工神经网络——从算法到实现：教程

本教程回顾了人工神经网络在光子学中的应用，目标受众广泛，从光学研究和工程社区到计算机科学和应用数学。我们在这里重点关注这些学科之间的交叉领域的研究领域，试图在每个领域特定的技术细节和整体清晰度之间找到适当的平衡。首先，我们简要回顾一些核心神经网络类型的关键属性和特性，我们认为它们与光子学最相关，并将该层的理论设计与一些光子学硬件实现联系起来。之后，我们阐明了如何微调所选模型的设计以以优化的精度执行所需任务的问题。然后，在点评部分，我们讨论神经网络在光子学中的几个选定应用的最新发展和进展，包括与光通信、成像、传感以及新材料和激光器设计相关的多个方面。在下面的部分中，我们特别强调如何在从算法到硬件实现的过渡的背景下准确评估神经网络的复杂性。引入的复杂性特征用于分析神经网络在光通信中的应用，作为一个特定但非常重要的示例，将其与一些基准信号处理方法进行比较。我们将机器学习中使用的众所周知的模型压缩策略的描述与最近在神经网络的光学应用中引入的一些新技术结合起来。