Your article bolds the following in the introductory paragraph: > My final classifier had accuracy of ~97% on MNIST and it refused to classify white noise and

Your article bolds the following in the introductory paragraph: > My final classifier had accuracy of ~97% on MNIST and it refused to classify white noise and the majority of unrelated (non-MNIST) images. I'm saying that you are making a misleading claim when you say this. It is categorically untrue that you achieve 97% on MNIST with this approach. Accuracy on a standardized test set makes no sense when you select a subset from the test set for maximal accuracy. I'm not saying that your approach doesn't have value or make sense in some business cases, because I think the overall idea is interesting but these statements are not correct.