Summary
In this chapter, we covered some deep learning networks that were not covered in earlier chapters. We started with a brief look into the Keras functional API, which allows us to build networks that are more complex than the sequential networks we have seen so far. We then looked at regression networks, which allow us to do predictions in a continuous space, and opens up a whole new range of problems we can solve. However, a regression network is really a very simple modification of a standard classification network. The next area we looked at was autoencoders, which are a style of network that allows us to do unsupervised learning and make use of the massive amount of unlabeled data that all of us have access to nowadays. We also ...
Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.
Read now
Unlock full access