Why is scaling important in deep learning?
Scaling is crucial because it unlocks emergent properties in neural networks. We've observed that as models grow larger and are trained on more data, their performance on a wide range of tasks improves dramatically, often in predictable ways. This scaling principle applies to both the size of the neural network (number of parameters) and the quantity and diversity of training data. It's about finding the right computational resources to reveal inherent learning capacities.
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