Older learning algorithms such as logistic regression can plateau as more data is added, meaning their learning curves flatten and the algorithms stop improving even with more data.

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Recent progress in deep learning has been driven by data availability and computational scale.

Scale Drives Machine Learning Progress

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Data availability is a major driver of recent deep learning progress because digital-device activity creates large amounts of data that can be fed to learning algorithms.

Data Availability as a Driver of Deep Learning Progress

Computational scale is a major driver of recent deep learning progress because it makes it possible to train neural networks large enough to take advantage of huge datasets.

Computational Scale as a Driver of Deep Learning Progress

Older Learning Algorithms Can Plateau with More Data

Larger neural networks can obtain better performance, and the best performance comes from training a very large neural network while also having a huge amount of data.

Large Neural Networks Benefit from Huge Data

In a small data regime, traditional algorithms may or may not do better depending on how features are hand-engineered. With only about 20 training examples, feature hand-engineering can matter more than choosing logistic regression or a neural network.

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