4 ms·

> Commonly understood in the field^ is that 60,000 examples is the sweet spot for training and validation data, 50k for training 10k for test/validation. I don

• amelius 9y ago
  No, the deep learning network should be smart enough to reduce the data to its essence (and that's what it ultimately does). If DL would need more training data for higher dimensional inputs, then DL would lose against a simple pattern matching (correlation) algorithm at some point.
  • landon32 9y ago
    Imagine you are trying to use a neural network to classify single bit data as either being 1 or 0 (I know you obviously don't need a neural net for this, but it's an example). Aside from not needing a very deep network, you would not need much training data. Then imagine classifying the color of a single pixel as "light" or "dark". There's three dimensions-- red, green, and blue. You would also need much less training data here than if you were trying to train a network to recognize a car, right? I think this is what zeroxfe is referring to