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“The number of places where machine learning can be used effectively from both a cost perspective and a return perspective are small.” Thankfully transfer lear
by andreilys 7y ago
“The number of places where machine learning can be used effectively from both a cost perspective and a return perspective are small.”
Thankfully transfer learning and super convergence invalidates this claim.
Using pre-trained models + specific training techniques significantly reduces the amount of data you need, your training time and the cost to create near state of the art models.
Both Kaggle and google colab offer free GPU.
- ska 7y ago>Thankfully transfer learning and super convergence invalidates this claim. IME it is nowhere near as universally successful as this suggests.
- craftinator 7y ago> Both Kaggle and google colab offer free GPU. I think this sentence invalidates your argument against: “The number of places where machine learning can be used effectively from both a cost perspective and a return perspective are small.” In a hobbyist world, free GPU time is an amazing thing, and you can do a lot of fun and rewarding projects using transfer learning and other techniques that avoid heavy engineering and data processing. In a business world, where your product must consistently and accurately perform well, problems that may be solved by ML need to be heavily scrutinized and researched, because for most problems there are cheaper, faster, more robust solutions. Free GPU time doesn't weigh in at this scale.
- andreilys 7y agoSure if ML/DL is your core business than yeah it doesn't make sense. If ML/DL is an add-on to help augment your business (separate from the core value) then yes transfer learning and free GPU's will get you good returns.