unsupervised learning using tensorflow and keras autoencoders neural networks tensorflow keras autoencoderthe encoder and the decoder undercomplete autoencoders overcomplete autoencoders dense vs sparse autoencoders denoising autoencoder variational autoencoder conclusion hands-on autoencoder data preparation the components of an autoencoder activation functions our first autoencoder loss function optimizer training the model evaluating on the test set two-layer undercomplete autoencoder with linear activation function increasing the number of nodes adding more hidden layers nonlinear autoencoder overcomplete autoencoder with linear activation overcomplete autoencoder with linear activation and dropout sparse overcomplete autoencoder with linear activation sparse overcomplete autoencoder with linear activation and dropout working with noisy datasets denoising autoencoder two-layer denoising undercomplete autoencoder with linear activation two-layer denoising overcomplete autoencoder with linear activation two-layer denoising overcomplete autoencoder with relu activation conclusion semisupervised learning data preparation supervised model unsupervised model table of contents vii