Data and Statistical Sciences, AbbVie Inc., North Chicago, Illinois, USA.
Stat Med. 2022 Nov 20;41(26):5365-5378. doi: 10.1002/sim.9564. Epub 2022 Aug 30.
Deep learning is a subfield of machine learning used to learn representations of data by successive layers. Remarkable achievements and breakthroughs have been made in image classification, speech recognition, et cetera, but the full capability of deep learning is still under exploration. As statistical researchers and practitioners, we are especially interested in leveraging and advancing deep learning techniques to address important and impactive problems in biomedical and other related fields. In this article, we provide a basic introduction to Feedforward Neural Networks (FNN) along with some intuitive explanations behind its strong functional representation. Guidance is provided on how to choose quite a few hyperparameters in neural networks for a specific problem. We further discuss several more advanced frameworks in deep learning. Some successful applications of deep learning in biomedical fields are also demonstrated. With this beginner's guide, we hope that interested readers can include deep learning in their toolbox to tackle future real-world questions and challenges.
深度学习是机器学习的一个子领域,用于通过连续的层来学习数据的表示。在图像分类、语音识别等方面已经取得了显著的成就和突破,但深度学习的全部能力仍在探索之中。作为统计研究人员和从业者,我们特别有兴趣利用和推进深度学习技术,以解决生物医学和其他相关领域的重要和有影响力的问题。在本文中,我们提供了前馈神经网络(FNN)的基本介绍,并对其强大的功能表示背后的一些直观解释。针对特定问题,我们提供了如何选择神经网络中相当多的超参数的指导。我们进一步讨论了深度学习中的几个更高级的框架。还展示了深度学习在生物医学领域的一些成功应用。有了这个初学者指南,我们希望有兴趣的读者可以将深度学习纳入他们的工具包,以解决未来的现实世界问题和挑战。