CEST Imaging requires radio frequency pulses at several frequency offsets to generate CEST maps (APT, NOE, DS, MT). In this study, we aimed to generate APT maps from CEST images of undersampled frequency offsets using deep learning, which can potentially reduce the total scan time of CEST imaging. The Z-spectrum was undersampled by a factor of 3.5 using the Fisher information gain analysis. Fitting results from the proposed method were compared with those from multi-pool fitting with fully sampled Z-spectrum. We have shown that it is feasible to reconstruct APT maps from undersampled, uncorrected CEST images.