the large data set and just adapt it to your small data set and that usually it tends to work pretty well this is called transfer learning this step here is called fine-tuning sometimes and it's sort of the approach that comes from natural language processing from these big transformers like Bert where you first train on a really big data set that might not be the data set that you want in the end but it's really big so you can sort of learn a lot of things from that data set and then the only thing left to do is to fine tune it to