Lay abstract for Statistics in Medicine article: DL 101: Basic introduction to deep learning with its application in biomedical related fields

Each week, we publish lay abstracts of new articles from our prestigious portfolio of journals in statistics. The aim is to highlight the latest research to a broader audience in an accessible format.
This Tutorial in Biostatistics featured today is from Statistics in Medicine and is now available to read in full here.
Zhan, TDL 101: Basic introduction to deep learning with its application in biomedical related fieldsStatistics in Medicine20224126): 5365– 5378. doi:10.1002/sim.9564
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.   
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