[yonkshi]

I don't think most ML experts would agree with that, a big reason DL became popular are the huge improvements they brought to CV and NLP fields.

In many ways, traditional approaches were harder because you need huge amount of domain expertise in CV & NLP, whereas a ML expert can solve simple CV problems with almost no domain knowledge.

Now, a lot of the business data, especially time series data, I agree that an algorithm/heuristic approach is easier and more robust. E.g. recommendation systems.

[pedrosorio]

"traditional approaches" in CV & NLP were also ML (a quick reminder that machine learning existed long before the deep learning hype).

Not sure what the parent meant by "algorithmic approach" though.

[claytonjy]

yes, but before the ML step the old approaches relied on expert-crafted features. The breakthroughs in those fields via deep-learning is because people found architectures (CNN/RNNs) that could learn those features much, much, much more efficiently than they could be hand-crafted.

[sgt101]

Well, if you tune the hyperparameters and architecture just so....