Machine Learning with TensorFlow, Second Edition

12 Applying autoencoders: The CIFAR-10 image dataset

This chapter covers

• Navigating and understanding the structure of the CIFAR-10 image dataset
• Building an autoencoder model to represent different CIFAR-10 image classes
• Applying the CIFAR-10 autoencoder as an image classifier
• Implementing a stacked and denoising autoencoder on CIFAR-10 images

Autoencoders are powerful tools for learning arbitrary functions that transform input into output without having the full set of rules to do so. Autoencoders get their names from their function: learning a representation of the input much smaller than its size, which means encoding input data using less knowledge and then decoding that internal representation to get approximately back to its original ...