摘要翻译：
机器学习是解决问题和任务自动化的现代方法。特别是，机器学习关注于能够识别数据中的模式并将其用于预测建模的算法的开发和应用。人工神经网络是一种特殊的机器学习算法和模型，它演变成了现在被称为深度学习的东西。鉴于过去十年在计算方面取得的进步，深度学习现在可以应用于海量数据集和无数上下文中。因此，深度学习成为机器学习的一个子领域。在生物研究中，它越来越多地被用于从高维生物数据中获得新的见解。为了让对机器学习有一定经验的科学家更容易获得深度学习的生物学应用，我们征求了对生物学和深度学习有不同兴趣的研究人员的意见。这些人通过使用GitHub版本控制平台和Manubot手稿生成工具集合作完成了这篇手稿的写作。我们的目标是在使用深度学习时，阐明一套实用的、易于理解的、简明的指导方针和建议。在我们的讨论过程中，几个主题变得清晰起来：理解和应用机器学习基础作为利用深度学习的基线的重要性，广泛的模型比较和仔细评估的必要性，以及在解释深度学习产生的结果时批判性思维的必要性，等等。
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英文标题：
《Ten Quick Tips for Deep Learning in Biology》
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作者：
Benjamin D. Lee, Anthony Gitter, Casey S. Greene, Sebastian Raschka,
  Finlay Maguire, Alexander J. Titus, Michael D. Kessler, Alexandra J. Lee,
  Marc G. Chevrette, Paul Allen Stewart, Thiago Britto-Borges, Evan M. Cofer,
  Kun-Hsing Yu, Juan Jose Carmona, Elana J. Fertig, Alexandr A. Kalinin, Beth
  Signal, Benjamin J. Lengerich, Timothy J. Triche Jr, Simina M. Boca
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最新提交年份：
2021
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分类信息：

一级分类：Quantitative Biology        数量生物学
二级分类：Other Quantitative Biology        其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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英文摘要：
  Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Artificial neural networks are a particular class of machine learning algorithms and models that evolved into what is now described as deep learning. Given the computational advances made in the last decade, deep learning can now be applied to massive data sets and in innumerable contexts. Therefore, deep learning has become its own subfield of machine learning. In the context of biological research, it has been increasingly used to derive novel insights from high-dimensional biological data. To make the biological applications of deep learning more accessible to scientists who have some experience with machine learning, we solicited input from a community of researchers with varied biological and deep learning interests. These individuals collaboratively contributed to this manuscript's writing using the GitHub version control platform and the Manubot manuscript generation toolset. The goal was to articulate a practical, accessible, and concise set of guidelines and suggestions to follow when using deep learning. In the course of our discussions, several themes became clear: the importance of understanding and applying machine learning fundamentals as a baseline for utilizing deep learning, the necessity for extensive model comparisons with careful evaluation, and the need for critical thought in interpreting results generated by deep learning, among others.
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PDF链接：
https://arxiv.org/pdf/2105.14372