Useful new tip I learned from #practicaldeeplearning by @jh :
Train a model _before_ data cleaning to identify the items that are the most troublesome.
This might also help you identify the types of problems that are occurring so that next time it can help you in collecting better datasets.
So I’ve been wondering what problem to work on for my learning while I work my way through #practicaldeeplearning. I think I’ve found the perfect dataset: https://figshare.com/collections/_/4560497
It’s got everything. The raw 12-lead ECG readings as well as arrhythmia classifications for over 10,000 patients. And best of all it’s easy to convert the ECG readings to images for #deeplearning. Will probably start by trying out #ResNet initially.
Will keep posting my progress here.