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Though initially outclassed by more computationally efficient machine learning algorithms, neural network–based deep learning strategies have come of age with modern implementations and hardware and are now capable of addressing a range of previously intractable problems. These include methods for dealing with complex spatial or sequential data, strategies for coping with sets of data in which not all of the examples are annotated, and ways of generalizing across different databases. Other methods take inspiration from biological strategies such as operant conditioning or evolutionary principles. These and other techniques described here form the basis for many of the advances in the use of artificial intelligence in the service of pathology, as well as medicine in general.