Deep learning for jet-tagging and jet calibration have recently been increasingly explored. For jet-flavor tagging CMS’s most performant tagger for 2016 data (DeepCSV) was based on a deep neural network. The input was a set of standard tagging variables of pre-selected objects. For 2017 improved algorithms are implemented that start from particle candidates without much preselection, i.e. much more raw and unfiltered data. Significantly better tagging is achieved especially in the boosted regime of high transverse momentum. The presentation will include flavor tagging and further newest public results on deep-learning for jet tagging/calibration. The presenter will discuss the neural network structures used that capture the structure of CMS jet-data and lead to the performance boost.