The minimum requirements to successfully apply deep learning depends on the problem you're trying to solve. In contrast to static, benchmark datasets like MNIST and CIFAR-10, real-world data is messy, varied and evolving, and that is the data practical deep learning solutions must deal with. Deep learning can be applied to any data type. The data types you work with, and the data you gather, will depend on the problem you're trying to solve. Deep learning can solve almost any problem of machine perception, including classifying data, clustering it, or making predictions about it.