Autoencoders can be successfully applied in many use cases, and hence, have gained much popularity in the world of deep learning. In this section, we will discuss the important applications and uses of autoencoders:
Dimensionality reduction: If you remember, in Chapter 1, Introduction to Deep Learning, we introduced the concept of the 'curse of dimensionality'. Dimensionality reduction was one of the first applications of deep learning. Autoencoders were initially studied to overcome the issues with the curse of dimensionality. We have already got a fair idea from this chapter how deep autoencoders work on higher-dimensional data to reduce the dimensionality in the final output.
Information Retrieval: One more important application of autoencoders is in information retrieval. Information retrieval basically means to search for some entries, which match with an entered query, in a database. Searching in high-dimensional data is generally a cumbersome task; however...
