I recently started reading “Deep Learning with R”, and I’ve been really impressed with the support that R has for digging into deep learning. One of the use cases presented in the book is predicting prices for homes in Boston, which is an interesting problem because homes can have such wide variations in values. This is a machine learning problem that is probably best suited for classical approaches, such as XGBoost, because the data set is structured rather than perceptual data. However, it’s also a data set where deep learning provides a really useful capability, which is the ease of writing new loss functions that may improve the performance of predictive models. The goal of this post is to show how deep learning can potentially be used to improve shallow learning problems by using custom loss functions.