We present and describe a catalog of galaxy photometric redshifts (photo-z's) for the Sloan Digital Sky Survey (SDSS) Coadd Data. We use the Artificial Neural Network (ANN) technique to calculate photo-z's and the Nearest Neighbor Error (NNE) method to estimate photo-z errors for ∼ 13 million objects classified as galaxies in the coadd with r < 24.5. The photo-z and photo-z error estimators are trained and validated on a sample of ∼ 83,000 galaxies that have SDSS photometry and spectroscopic

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... shifts measured by the SDSS Data Release 7 (DR7), the Canadian Network for Observational Cosmology Field Galaxy Survey (CNOC2), the Deep Extragalactic Evolutionary Probe Data Release 3(DEEP2 DR3), the VIsible imaging Multi-Object Spectrograph - Very Large Telescope Deep Survey (VVDS) and the WiggleZ Dark Energy Survey. For the best ANN methods we have tried, we find that 68 validation set have a photo-z error smaller than σ_68 =0.031. After presenting our results and quality tests, we provide a short guide for users accessing the public data.