Deep Learning Basics with Python, TensorFlow and Keras
I know a machine learning engineer from a certain massive lending company. I asked him “How do you decide which strategy to use and which algorithms to pay attention to? There are so many, and it’s hard to figure out which ones are production grade.”He said gradient boosting. I pointed out that that implies there would be some period of time where the algorithm makes bad decisions. He said yup, that’s why it’s hard to start a lending company. Gotta find the data on the bad performers.Apparently their original decision model was just a hand crafted set of if-statements.I suspect most ML endeavors that seem impressive have origin stories like that, but it’s only anecdata.