Summary

Deep learning has made significant contributions to the field of unsupervised learning, leading to the development of several innovative methods. But by far the most impactful method is unsupervised pretraining, which leverages the abundance of free data available on the internet today to improve the model performance of the downstream supervised tasks and create generalizable representations. With deeper research and time, unsupervised learning will aid in closing the gap toward general artificial intelligence. Overall, deep learning has been a valuable tool in the unsupervised learning domain, helping practitioners make the most of the large amounts of free data available on the internet today.

In the next chapter, we will dive into the first chapter of the second part of this book, which is meant to introduce methods that provide insights about a trained deep learning model.