Right now, there’s a Kaggle competition running about trying to predict credit delinquency. The problem, thus, is a classification one with structured data. A specific problem that is never actually used in the Deep Learning Course. You will see then why this is a perfect competition to get out of our ML comfort zone.
I just created a Kaggle kernel (I had troubles with the Kaggle Kernel, so I created another one) with the bare basics to start using Neural Networks on the problem. I had to tweak the code from the Rossman lectura a little bit but I finally got it running with categorical embeddings and a weighted loss function to try to account for the class imbalance.
Right now, this model is still lagging, as all the others kernels using Neural Networks, with respect to Boosting Trees. If you have any recommendations or questions, I’d be happy to discuss them!