论文标题
审查和前景：核磁共振光谱中的深度学习
Review and Prospect: Deep Learning in Nuclear Magnetic Resonance Spectroscopy
论文作者
论文摘要
由于深度学习的概念（DL）是在2006年正式提出的，因此对学术研究和行业产生了重大影响。如今，DL提供了一种前所未有的方法来分析和处理数据，并在计算机视觉，医学成像，自然语言处理等方面表现出了很大的结果。在此MinireView中，我们总结了DL在核磁共振（NMR）光谱频谱（NMR）光谱中的应用，并概述了DL的观点，即DL的全新方法，这些方法可能会更加强大地转化化学范围，以更大程度地转化了nmr Spectry spectry spection and nmr spectry spectry spection and nmr spectry spection seversion soperif consecy and nmr spection soppy soperif的效果和范围均高效。
Since the concept of Deep Learning (DL) was formally proposed in 2006, it had a major impact on academic research and industry. Nowadays, DL provides an unprecedented way to analyze and process data with demonstrated great results in computer vision, medical imaging, natural language processing, etc. In this Minireview, we summarize applications of DL in Nuclear Magnetic Resonance (NMR) spectroscopy and outline a perspective for DL as entirely new approaches that are likely to transform NMR spectroscopy into a much more efficient and powerful technique in chemistry and life science.
