With BPROP training I get 2.3% of errors in MNIST with just 1000 hidden units 2-layers net, no convolution layers. Pretty good AFAIK.
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then you ask the network given this user, what AD to show...
looks like it would sometimes be a classification problem and sometime a regression problem. You need support for multiple losses.
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Yep… one size fits all is hard. However fortunately it’s pretty easy to document this, and to pick a sane default.
Thanks. Twitter will use this to make your timeline better. UndoUndo
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in theory Redis could guess after a few tries in most cases? If the “teachers” outputs always look like all 0s but a single 1…
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black box ML is a very difficult problem. One size fits all is impossible, you need to constrain the problem upstream.
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