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Deep Learning for Tabular Data: A Bag of Tricks | ODSC 2020

Jason McGhee, Senior Machine Learning Engineer at DataRobot, has been spending time applying deep learning and neural networks to tabular data. Although the deep learning technique can prove challenging, his research supports how valuable it is when using tabular datasets. In this video (adapted from his presentation at ODSC Boston 2020), Jason shares some important techniques for implementing deep learning when learning heterogenous tabular data. Learn more about Jason’s findings and ask him questions at his DataRobot Community post: https://community.datarobot.com/t5/ai... Table of Contents Motivation: 0:15 Impute missing values: 1:37 Prepare categoricals, text, and numerics: 2:49, 3:10, 3:31 Properly validate: 3:54 Establish a benchmark: 5:24 Start with a low capacity network: 6:10 Determine output activation and loss function for classification and regression: 7:17, 8:26 Determine hidden activation: 9:46 Choose batch size: 10:57 Build learning rate schedule: 12:02 Determine number of epochs: 14:35 Track and interpret regression predictions: 15:30 Track metric and/or loss: 16:09 Track and interpret classification predictions: 16:45 Benchmark the network: 17:11 Dealing with discontinuities: 18:16 Tuning the network: 19:31 Handing overfitting vs. underfitting: 20:41 All tricks in one place: 21:35 Music for this video: https://www.bensound.com. Stay connected with DataRobot! Blog: https://blog.datarobot.com/ Community: https://community.datarobot.com/ Twitter:   / datarobot   LinkedIn: h  / datarobot   Facebook:   / datarobotinc   Instagram:   / datarobot