generative deep learning random vector from the latent space generated (decodedimage generator (decoderdiscriminator training feedback "real,"fakemix of real and fake images figure generat or ransforms random lat ent vect ors int imagesand discriminator seeks to ell real images from generated ones the generat or is trained fool he discriminat or remarkablya gan is system where the optimization minimum isn' fixedunlike in any other training setup you've encountered in this book normallygradient descent consists of rolling down hills in static loss landscape but with ganevery step taken down the hill changes the entire landscape little it' dynamic system where the optimization process is seeking not minimumbut an equilibrium between two forces for this reasongans are notoriously difficult to train--getting gan to work requires lots of careful tuning of the model architecture and training parameters figure lat ent space dwellers images generat ed by mike tyka using multistaged gan trained on dat aset of faces (www miket yka comlicensed to