Deep learning was recently successfully used in deriving symmetry transformations that preserve important physics quantities. Being completely agnostic. these techniques postpone the identification of the discovered symmetries to a later stage. In this letter we propose methods for examining and identifying the group-theoretic structure of such machine-learned symmetries. https://www.ivoryjinelle.com/limited-deal-University-of-California-Santa-Barbara-UCSB-Gauchos-Blue-Nintendo-Skins-p50420-hot-buy/