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This is a good overview of the landscape. From what I see, even if there's an AI bubble burst in funding, a lot of the ML techniques and algorithms can still b
by ww520 6y ago
This is a good overview of the landscape. From what I see, even if there's an AI bubble burst in funding, a lot of the ML techniques and algorithms can still be really useful for general computing with a smaller budget. Deep learning with neural net might be too costly to run for most projects. Shallow learning algorithms are a lot more cost effective to add to general computing. Algorithms like Support Vector Machine or Decision Tree learning work surprisingly well in targeted areas.
- visarga 6y agoThey are OK if your problem is based on tabular data, but not for images and text. But the current paradigm is to reuse a pre-trained model, it's less more data intensive. In some tasks if you have a good backbone model you just need one or a few training examples.
- IanCal 6y agoSVMs absolutely work on text, TF-IDF + SVM is a very classic (and pretty solid) approach for classification. It's easily explainable and has known classes of problems.