what i tell them: "most dl/ml projects tend to turn into building infrastructure to handle your data". what they say: "oh, we are HANDLING our data" => interpretation: we have data and we do _something_ with it.
. having data and moving it around != solid data infra
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what I tell them: "build simple baselines on top of tired and true information theory and computer science, it will help you understand your data better AND provide a baseline IF you need to do more". what they say: "but deep learning!"
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me: "where is the training data?", them: "oh we have data!" me: "How was it labeled, who did it, when?". them: "
what you mean 'labeled'?..." me: "if you don't have training data you do it the _normal_ way: extract features, measure, use measurements to evaluate" them: "but dl"1 reply 0 retweets 2 likesShow this thread
Replying to @jbowayles
It hasn't been stressed enough, not even close.
11:05 AM - 1 Jan 2019
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Writing about stuff to learn how it works, mostly in Rust.
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