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I was going to say something around those lines but you save me the effort. Although in my opinion using ML and have some decent results is not that hard. Use
by pmelendez 13y ago
I was going to say something around those lines but you save me the effort.
Although in my opinion using ML and have some decent results is not that hard. Use it and being close to the state in the art (i.e improving your recognition rate from 65% to 80%) is a completely different matter and it is hard and it requires a fully understanding of the details of the algorithm in use.
Still it doesn't meant that you can't do interesting things with an overall understanding of machine learning, but making a good cat detector is hard [0] [1]
[0] http://research.google.com/archive/unsupervised_icml2012.html http://research.google.com/archive/unsupervised_icml2012.htm...
[1] http://techtalks.tv/talks/machine-learning-and-ai-via-brain-simulations/57862/ http://techtalks.tv/talks/machine-learning-and-ai-via-brain-...
- dylandrop 13y agoFor a 65% to 80% jump, I'd say it depends on the subfield. With NLP (which is basically applied machine learning), it's not uncommon to see algorithms with >80% accuracy.