Summary

Congratulations! You completed the final chapter on deep learning.

We started this chapter by discussing what semantic segmentation means, then we talked extensively about DenseNet and why it is such a great architecture. We quickly talked about using a stack of convolutional layers to implement semantic segmentation, but we focused on a more efficient way, which is using DenseNet after adapting it to this task. In particular, we developed an architecture similar to FC-DenseNet. We collected a dataset with the ground truth for semantic segmentation, using Carla, and then we trained our neural network on it and saw how it performed and when detecting roads and other objects, such as pedestrians and sidewalks. We even discussed a trick to improve the output of a bad semantic segmentation.

This chapter was quite advanced, and it required a good understanding of all the previous chapters about deep learning. It has been quite a ride, and I think it is fair to say that this...