What is the main purpose of increased computational scale in deep learning?

A small gap between training error and training-dev error suggests the model generalizes similarly to unseen data from the same distribution.

Why larger compute can improve deep learning

Match each term to its role in explaining why larger compute can accelerate deep learning gains.

Put the steps in order: why more compute can improve deep learning results

When did it become practical to train neural networks large enough to benefit from very large datasets?

Computing power by itself is enough to explain recent deep learning progress.

Neural Network Size and Data

Match each development factor with the limitation it removes in modern deep learning.

Order the actions a practitioner would take when trying to use compute to benefit from a very large dataset.

How More Compute Helps When Training on Very Large Datasets

Choosing Model and Compute Capacity for a Very Large Dataset

Why More Compute Helps Deep Learning Use Large Data