The field of deep learning has positioned itself in the past decade as a prominent and extremely fruitful engineering discipline. This comeback of neural networks in the early 2000s swept the machine learning community, and soon after found itself immersed in practically every scientific, social, and technological front. A growing series of contributions established this field as leading to state-of-the-art results in nearly every task, recognizing image content, understanding written documents, exposing obscure connections in massive datasets, facilitating efficient search in large repositories, translating languages, enabling a revolution in transportation, revealing new scientific laws in physics and chemistry, and so much more. Deep neural networks not only solve known problems but offer, in addition, unprecedented results in deploying learning to problems that until recently were considered as hopeless or only weakly successful. These include automatically synthesizing text–media, creating musical art pieces, synthesizing realistic images and video, enabling competitive game-playing, and this list goes on and on.

中文翻译：

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迈向深层神经网络神秘化的又一步[应用数学]

在过去的十年中，深度学习领域已将自己定位为杰出且卓有成效的工程学科。在2000年代初，神经网络的卷土重来席卷了机器学习社区，不久之后，它便陷入了几乎所有科学，社会和技术领域。越来越多的贡献确立了该领域的领先地位，使之能够在几乎每项任务中取得最先进的成果，包括识别图像内容，理解书面文件，暴露海量数据集中模糊的联系，促进在大型存储库中进行高效搜索，翻译语言，实现交通革命，揭示了物理和化学领域的新科学定律，等等。深度神经网络不仅可以解决已知问题，而且还提供 在将学习部署到直到最近才被认为是绝望的或仅是微弱的成功的问题上取得了前所未有的成果。其中包括自动合成文本媒体，创建音乐作品，合成逼真的图像和视频，实现具有竞争力的游戏玩法，并且这个清单还在不断增加。