Today, machine learning applications touch almost every angle of business, science, and private life, ranging from speech and image recognition to generative models to improve drug design. Machine learning’s primary goal is to train computers to make sense of an ever-expanding pool of data. However, in order to learn from these increasingly complex datasets, the underlying models, such as deep neural networks, also become more sophisticated and expensive to train.

This results in complicated models with very long training times that risk over-fitting without sufficient generalization. In other words, we must be vigilant that our models meaningfully understand our data, rather than merely memorizing what they have already seen. Therefore, a lot of effort is put into improving training algorithms of models, as well as dedicated classical hardware. Read More

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