 This paper presents a tutorial on deep learning, DL, for digital pathology, DP, image analysis. The authors use an open source framework called CAFE to address various DP tasks such as nuclei segmentation, epithelium segmentation, tubule segmentation, lymphocyte detection, mitosis detection, invasive ductal carcinoma detection, and lymphoma classification. They show that DL approaches can produce comparable or superior results to handcrafted feature-based classification approaches. The paper provides step-by-step instructions for the usage of the supplied source code, trained models, and input data. This article was authored by Andrew Janowczyk and Anant Matabusi. We are article.tv, links in the description below.