> "Training a model is skipping to executing the function, without first learning how to write it. Is training a model even a feat worth bragging about?" do yo

> "Training a model is skipping to executing the function, without first learning how to write it. Is training a model even a feat worth bragging about?" do you really think its easy? go do kaggle competitions. if its really easy you should just be able to run a few models and quit your day job. ML and statistics are very much the same and just as different. maybe its true about ML people as well, but statisticians have a dogma. they think there is a right model, and they came up with it. ML practitioners generally think they have a more useful model than what was previously done. I had a PHD in statistics try to do deep learning on images. they never got far because they could not get over the idea of having more parameters than observations. I had stats people ask me what is the r^2 on the results of an xgboost model, which the best answer I could give them was that the mean was not a good representation of the data therefore any r^2 number could not be trusted, to which they hated.