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We present morphological classifications of $\sim$27 million galaxies fromthe Dark Energy Survey (DES) Data Release 1 (DR1) using a supervised deeplearning algorithm. The classification scheme separates: (a) early-typegalaxies (ETGs) from late-types (LTGs); and (b) face-on galaxies from edge-on.Our Convolutional Neural Networks (CNNs) are trained on a small subset of DESobjects with previously known classifications. These typically have$\mathrm{m}_r \lesssim 17.7~\mathrm{mag}$; we model fainter objects to$\mathrm{m}_r < 21.5$ mag by simulating what the brighter objects with welldetermined classifications would look like if they were at higher redshifts.The CNNs reach 97\% accuracy to $\mathrm{m}_r<21.5$ on their training sets,suggesting that they are able to recover features more accurately than thehuman eye. We then used the trained CNNs to classify the vast majority of theother DES images. The final catalog comprises five independent CNN predictionsfor each classification scheme, helping to determine if the CNN predictions arerobust or not. We obtain secure classifications for $\sim$ 87\% and 73\% of thecatalog for the ETG vs. LTG and edge-on vs. face-on models, respectively.Combining the two classifications (a) and (b) helps to increase the purity ofthe ETG sample and to identify edge-on lenticular galaxies (as ETGs with highellipticity). Where a comparison is possible, our classifications correlatevery well with S\'ersic index (\textit{n}), ellipticity ($\epsilon$) andspectral type, even for the fainter galaxies. This is the largest multi-bandcatalog of automated galaxy morphologies to date.

Pushing automated morphological classifications to their limits with the Dark Energy Survey