This chapter showed that autoencoders are very simple models that can be used to encode and decode data for different purposes, such as data compression, data visualization, and simply finding latent spaces where only important features are preserved. We showed that the number of neurons and the number of layers in the autoencoder are important for the success of the model. Deeper (more layers) and wider (more neurons) traits are often ingredients for good models, even if that leads to slower training times.
At this point, you should know the difference between supervised and unsupervised learning in terms of passive learning. You should also feel comfortable implementing the two basic components of an autoencoder: the encoder and the decoder. Similarly, you should be able to modify the architecture of an autoencoder to fine-tune it to achieve better performance. Taking the example we discussed in this chapter, you should be able to apply an autoencoder to a dimensionality reduction...