What does computational scale primarily enable in deep learning according to Machine Learning Yearning?

Computational scale enables training neural networks large enough to take advantage of huge datasets.

Computational scale drives deep learning progress because it enables training neural networks large enough to take advantage of _____ datasets.

Match each term to its role in explaining why computational scale drives deep learning progress.

Order the reasoning steps that explain how computational scale leads to improved deep learning performance.

According to Machine Learning Yearning, how recently did it become possible to train neural networks large enough to exploit huge datasets?

Computational scale alone, without large datasets, is sufficient to drive recent deep learning progress.

Machine Learning Yearning (p. 9): 'We started just a few years ago to be able to train neural networks that are _____ enough to take advantage of the huge datasets we now have.'

Match each factor to the specific bottleneck it overcomes in enabling modern deep learning progress.

Order the steps a practitioner would follow when applying the insight that computational scale drives deep learning progress.

Analyzing the Relationship Between Computational Scale and Large Datasets

Scaling Decision for Deep Learning with Massive Datasets

The Role of Computational Scale in Utilizing Huge Datasets