I'm an ML engineer, and I agree with you- deep learning is by far the most common approach for new problems in informatics.
Imo deep learning is so popular because it "works". For a classification problem, if you try a linear baseline and a deep learning model, and you do a reasonable job of hyperparameter tuning and experimental design, it's likely you will outperform a simpler model. This holds true across many problem spaces.
I think the issue is that modern DL frameworks make it a little too easy to get pretty good performance on new problems. Other techniques generally require more background knowledge to make reasonable modeling assumptions, and still frequently perform worse than a naively applied DL approach.
I think DL will remain, in practice and education, a very popular tool. But it is essential to learn traditional statistical inference and other background to appropriately contextualize DL models so it isn't just some form of black magic.
Imo deep learning is so popular because it "works". For a classification problem, if you try a linear baseline and a deep learning model, and you do a reasonable job of hyperparameter tuning and experimental design, it's likely you will outperform a simpler model. This holds true across many problem spaces.
I think the issue is that modern DL frameworks make it a little too easy to get pretty good performance on new problems. Other techniques generally require more background knowledge to make reasonable modeling assumptions, and still frequently perform worse than a naively applied DL approach.
I think DL will remain, in practice and education, a very popular tool. But it is essential to learn traditional statistical inference and other background to appropriately contextualize DL models so it isn't just some form of black magic.