Differential ML on TensorFlow and Colab - DataScienceCentral.com

Brian Huge and I just posted a working paper following six months of research and development on function approximation by artificial intelligence (AI) in Danske Bank. One major finding was that training machine learning (ML) models for regression (i.e. Given those differential labels, we can write simple, yet unreasonably effective training algorithms, capable of learning accurate function approximations with remarkable speed and accuracy from small datasets, in a stable manner, without the need of additional regularization or optimization of hyperparameters, e.g. by cross-validation. In this post, we briefly summarize these algorithms under the name differential machine learning, highlighting the main intuitions and benefits and commenting TensorFlow implementation code. All the details are found in the working paper, the online appendices and the Colab notebooks.