Deep learning has gained tremendous traction from the developer and researcher communities. It plays an increasingly significant role in a number of application domains. Deep learning frameworks are proposed to help developers and researchers easily leverage deep learning technologies, and they attract a great number of discussions on popular platforms, i.e., Stack Overflow and GitHub. To understand and compare the insights from these two platforms, we mine the topics of interests from these two platforms. Specifically, we apply Latent Dirichlet Allocation (LDA) topic modeling techniques to derive the discussion topics related to three popular deep learning frameworks, namely, Tensorflow, PyTorch and Theano. Within each platform, we compare the topics across the three deep learning frameworks. Moreover, we make a comparison of topics between the two platforms. Our observations include 1) a wide range of topics that are discussed about the three deep learning frameworks on both platforms, and the most popular workflow stages are Model Training and Preliminary Preparation. 2) the topic distributions at the workflow level and topic category level on Tensorflow and PyTorch are always similar while the topic distribution pattern on Theano is quite different. In addition, the topic trends at the workflow level and topic category level of the three deep learning frameworks are quite different. 3) the topics at the workflow level show different trends across the two platforms. e.g., the trend of the Preliminary Preparation stage topic on Stack Overflow comes to be relatively stable after 2016, while the trend of it on GitHub shows a stronger upward trend after 2016. Besides, the Model Training stage topic still achieves the highest impact scores across two platforms. Based on the findings, we also discuss implications for practitioners and researchers.

中文翻译：

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程序员对深度学习框架的讨论是什么

深度学习已经从开发人员和研究人员社区获得了巨大的吸引力。它在许多应用领域发挥着越来越重要的作用。提出深度学习框架是为了帮助开发人员和研究人员轻松利用深度学习技术，它们在流行平台（即 Stack Overflow 和 GitHub）上引起了大量讨论。为了理解和比较这两个平台的见解，我们从这两个平台挖掘感兴趣的话题。具体来说，我们应用潜在狄利克雷分配 (LDA) 主题建模技术来推导出与三个流行的深度学习框架相关的讨论主题，即 Tensorflow、PyTorch 和 Theano。在每个平台内，我们比较了三个深度学习框架的主题。而且，我们对两个平台之间的主题进行了比较。我们的观察包括 1) 在两个平台上讨论的关于三个深度学习框架的广泛主题，最流行的工作流程阶段是模型训练和初步准备。2）Tensorflow 和 PyTorch 的工作流级别和主题类别级别的主题分布始终相似，而 Theano 上的主题分布模式则大不相同。此外，三个深度学习框架在工作流层面和主题类别层面的主题趋势也大不相同。3）工作流层面的话题在两个平台上呈现出不同的趋势。例如，2016年以后Stack Overflow上Preliminary Prepared阶段的话题趋势相对稳定，GitHub 上的趋势在 2016 年之后呈现出更强的上升趋势。此外，模型训练阶段的主题仍然在两个平台上获得了最高的影响分数。基于这些发现，我们还讨论了对从业者和研究人员的影响。