contents generating images with variational autoencoders sampling from latent spaces of images concept vectors for image editing variational autoencoders wrapping up introduction to generative adversarial networks schematic gan implementation bag of tricks the generator the discriminator the adversarial network how to train your dcgan wrapping up summary conclusions key concepts in review various approaches to ai what makes deep learning special within the field of machine learning how to think about deep learning key enabling technologies the universal machine-learning workflow key network architectures the space of possibilities the limitations of deep learning the risk of anthropomorphizing machine-learning models local generalization vs extreme generalization wrapping up the future of deep learning models as programs beyond backpropagation and differentiable layers automated machine learning lifelong learning and modular subroutine reuse the long-term vision staying up to date in fast-moving field practice on real-world problems using kaggle read about the latest developments on arxiv explore the keras ecosystem appendix appendix final words installing keras and its dependencies on ubuntu running jupyter notebooks on an ec gpu instance index licensed to