Deep learning belongs to the field of artificial intelligence, where machines perform tasks that typically require some kind of human intelligence. Deep learning tries to achieve this by mimicking the learning of a human brain. Similar to the basic structure of a brain, which consists of (billions of) neurons and connections between them, a deep learning algorithm consists of an artificial neural network, which resembles the biological brain structure. Mimicking the learning process of humans with their senses, deep learning networks are fed with (sensory) data, like texts, images, videos or sounds. These networks outperform the state-of-the-art methods in different tasks and, because of this, the whole field saw an exponential growth during the last years. This growth resulted in way over 10 000 publications per year in the last years. For example, the search engine PubMed alone, which covers only a sub-set of all publications in the medical field, provides over 11 000 results for the search term $'$deep learning$'$ in Q3 2020, and ~90% of these results are from the last three years. Consequently, a complete overview over the field of deep learning is already impossible to obtain and, in the near future, it will potentially become difficult to obtain an overview over a subfield. However, there are several review articles about deep learning, which are focused on specific scientific fields or applications, for example deep learning advances in computer vision or in specific tasks like object detection. With these surveys as a foundation, the aim of this contribution is to provide a first high-level, categorized meta-analysis of selected reviews on deep learning across different scientific disciplines and outline the research impact that they already have during a short period of time.

中文翻译：

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深度学习——对跨科学学科及其研究影响的精选评论的首次元调查

深度学习属于人工智能领域，其中机器执行通常需要某种人类智能的任务。深度学习试图通过模仿人脑的学习来实现这一目标。类似于大脑的基本结构，它由（数十亿）个神经元和它们之间的连接组成，深度学习算法由一个类似于生物大脑结构的人工神经网络组成。用感官模拟人类的学习过程，深度学习网络提供（感官）数据，如文本、图像、视频或声音。这些网络在不同任务中的表现优于最先进的方法，因此，整个领域在过去几年中呈指数增长。这种增长导致在过去几年中每年出版超过 10 000 份出版物。例如，仅搜索引擎 PubMed 仅涵盖医学领域所有出版物的一个子集，在 2020 年第三季度为搜索词 $'$deep learning$'$ 提供了超过 11 000 个结果，约 90%这些结果来自过去三年。因此，已经不可能获得对深度学习领域的完整概述，并且在不久的将来，获得对某个子领域的概述可能会变得困难。但是，有几篇关于深度学习的评论文章侧重于特定的科学领域或应用，例如计算机视觉中的深度学习进展或对象检测等特定任务。以这些调查为基础，本贡献的目的是提供第一个高水平、