In spite of showing unreasonable effectiveness in modalities like Text and Image, Deep Learning has always lagged Gradient Boosting in tabular data - both in popularity and performance. But recently there have been newer models created specifically for tabular data, which is pushing the performance bar. But popularity is still a challenge because there is no easy, ready-to-use library like Sci-Kit Learn for deep learning. PyTorch Tabular is a new deep learning library which makes working with Deep Learning and tabular data easy and fast. It is a library built on top of PyTorch and PyTorch Lightning and works on pandas dataframes directly. Many SOTA models like NODE and TabNet are already integrated and implemented in the library with a unified API. PyTorch Tabular is designed to be easily extensible for researchers, simple for practitioners, and robust in industrial deployments.

翻译：尽管在文本和图像等模式中表现出了不合理的效能,深层学习在列表数据中总是落后于“渐进式推力”――在受欢迎度和性能方面。但最近出现了一些专门为列表数据而创建的较新的模型,这正在推动业绩栏。但是,广度仍然是个挑战,因为没有像Sci-Kit Learning那样的容易、随时使用的深层次学习图书馆。PyTorch Tabulal是一个新的深层学习图书馆,它使得与深层学习和表格数据合作变得容易和快速。它是一个建在PyTorrch和PyTorrch Lightning之上的图书馆,直接建在pandas数据框上。许多SOTA模型,如NODE和TabNet,已经与统一的API集成一体,在图书馆中实施。PyTorrch Tabulal设计可以方便研究人员使用,对从业人员简单,在工业部署中也很活跃。