convolutional neural networks epoch [ / ]loss val accuracy( %epoch [ / ]loss val accuracy( %epoch [ / ]loss val accuracy( %epoch [ / ]loss val accuracy( %epoch [ / ]loss val accuracy( %epoch [ / ]loss val accuracy( %epoch [ / ]loss val accuracy( %epoch [ / ]loss val accuracy( %after epochsthe performance is roughly the performance would definitely improve after several more epochshoweverthe time required to train such network is expensive one question we might wonder is whether there is faster and easier alternative to speed this up as it turns outtransfer learning is available for our resource the amazing news about cnns is that once layer is trainedit can essentially be reused for another task the lower-level features--for examplecurvesedgesand circles--and several higher-level features are always common or similar for most computer vision tasks we mighthoweverneed to retrain the last few layers to tailor the network specifically for our use case stillthat brings huge relief when training large network