As one of the most rapidly developing artificial intelligence techniques, deep learning has been applied in various machine learning tasks and has received great attention in data science and statistics. Regardless of the complex model structure, deep neural networks can be viewed as a nonlinear and nonparametric generalization of existing statistical models. In this review, we introduce several popular deep learning models including convolutional neural networks, generative adversarial networks, recurrent neural networks, and autoencoders, with their applications in image data, sequential data and recommender systems. We review the architecture of each model and highlight their connections and differences compared with conventional statistical models. In particular, we provide a brief survey of the recent works on the unique overparameterization phenomenon, which explains the strengths and advantages of using an extremely large number of parameters in deep learning. In addition, we provide a practical guidance on optimization algorithms, hyperparameter tuning, and computing resources.

中文翻译：

---

从统计角度进行深度学习

作为最快速发展的人工智能技术之一，深度学习已应用于各种机器学习任务，并在数据科学和统计学中引起了极大的关注。无论复杂的模型结构如何，深度神经网络都可以视为现有统计模型的非线性和非参数概括。在本文中，我们介绍了几种流行的深度学习模型，包括卷积神经网络，生成对抗网络，递归神经网络和自动编码器，以及它们在图像数据，顺序数据和推荐系统中的应用。我们回顾了每个模型的体系结构，并强调了它们与常规统计模型相比的联系和差异。尤其是，我们提供了有关独特的超参数化现象的最新著作的简要调查，这解释了在深度学习中使用大量参数的优势和优势。此外，我们提供了有关优化算法，超参数调整和计算资源的实用指南。