Generative Adversarial Networks (GAN) and Autoencoders are the ones that come to mind. There are also all the models used in research leading up to the "deep le

Generative Adversarial Networks (GAN) and Autoencoders are the ones that come to mind. There are also all the models used in research leading up to the "deep learning" craze: Helmholtz machine, Boltzman machine, Deep belief networks. http://pyro.ai/examples/vae.html http://pyro.ai/examples/vae.html http://www.scholarpedia.org/article/Boltzmann_machine http://www.scholarpedia.org/article/Boltzmann_machine http://www.scholarpedia.org/article/Deep_belief_networks http://www.scholarpedia.org/article/Deep_belief_networks Geoff Hinton's Coursera course goes pretty deep on the unsupervised models https://www.coursera.org/learn/neural-networks https://www.coursera.org/learn/neural-networks Reinforcement learning probably counts too (Deep Q Learning, Policy Gradient, Actor-Critic networks might be equivalent to GANs?) http://pytorch.org/tutorials/intermediate/reinforcement_q_learning.html http://pytorch.org/tutorials/intermediate/reinforcement_q_le... https://github.com/vy007vikas/PyTorch-ActorCriticRL https://github.com/vy007vikas/PyTorch-ActorCriticRL