Interesting article, is it concluding that different small networks are formed for different types of problems that we are trying to solve with the larger network?
How is this different from overfitting though? (PS: Overfitting isn't that bad if you think about it, as long as the test dataset or inference time model is trying to solve problems in the supposedly large enough training dataset)
Such an insightful article. The tools are allowing us to 10x-100x productivity in shorter bursts, which makes total sense. There's a lot more to software engineering beyond those bits, and that's why the 10x engineer imposter syndrome.
Pretty cool use of NLP. I wonder if training schools can use this to train their students to improve their readback detection. IMO, one of the hardest things as a pilot in training in the first few months is getting feedback on my communication with the tower.
How does EB-2 NIW essentially work? Is it tied to a specific employer or can it be used to justify H1b renewal/transfer beyond the 6 year limit at a different workplace with the different role ?
How is this different from overfitting though? (PS: Overfitting isn't that bad if you think about it, as long as the test dataset or inference time model is trying to solve problems in the supposedly large enough training dataset)