fast.ai · Making neural nets uncool again

In machine learning and deep learning we can't do anything without data. So the people that create datasets for us to train our models are the (often under-appreciated) heroes. Some of the most useful and important datasets are those that become important "academic baselines"; that is, datasets that are widely studied by researchers and used to compare algorithmic changes. Some of these become household names (at least, among households that train models!), such as MNIST, CIFAR 10, and Imagenet. We all owe a debt of gratitude to those kind folks who have made datasets available for the research community.