generative deep learning wrapping up image generation with deep learning is done by learning latent spaces that capture statistical information about dataset of images by sampling and decoding points from the latent spaceyou can generate never-before-seen images there are two major tools to do thisvaes and gans vaes result in highly structuredcontinuous latent representations for this reasonthey work well for doing all sorts of image editing in latent spaceface swappingturning frowning face into smiling faceand so on they also work nicely for doing latent-space-based animationssuch as animating walk along cross section of the latent spaceshowing starting image slowly morphing into different images in continuous way gans enable the generation of realistic single-frame images but may not induce latent spaces with solid structure and high continuity most successful practical applications have seen with images rely on vaesbut gans are extremely popular in the world of academic research--at leastcirca - you'll find out how they work and how to implement one in the next section to play further with image generationi suggest working with the largescale celeb faces attributes (celebadataset it' free-to-download image dataset containing more than , celebrity portraits it' great for experimenting with concept vectors in particular--it definitely beats mnist tip licensed to