summary with creative applications of deep learningdeep networks go beyond annotating existing content and start generating their own you learned the followinghow to generate sequence dataone timestep at time this is applicable to text generation and also to note-by-note music generation or any other type of timeseries data how deepdream worksby maximizing convnet layer activations through gradient ascent in input space how to perform style transferwhere content image and style image are combined to produce interesting-looking results what gans and vaes arehow they can be used to dream up new imagesand how latent-space concept vectors can be used for image editing these few techniques cover only the basics of this fast-expanding field there' lot more to discover out there--generative deep learning is deserving of an entire book of its own licensed to