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It is rare that deep learning performs better than simple analysis and statistics. It is a lot more common that a data anomaly is caused by a bug in implementi

• mlthoughts2018 8y ago
  It depends on what you’re working on. For generic descriptive statistics, then I agree, and also that has nothing whatsoever to do with data science. If you’re trying to do reverse image search or machine translation or creating custom embeddings unique to your business problem at hand, then deep learning is hands down better. This bolsters my point as well. If you only hired “full stack” data scientists and you’re trusting them to correctly tell you if / how deep learning is applicable to a new problem, instead of hiring specialists who actually know how to systematically diagnose that situation, you’re setting yourself up to fail. You may already be too biased towards believing simpler things “should” do better, and you’ll take the full stack person’s inability to outperform with deep learning as if it is confirmatory evidence, when really all it is telling you is that you need a specialist.