In this chapter, we introduced convolutional neural networks. We talked about their main building blocks – convolutional and pooling layers – and we discussed their architecture and features. We discussed data pre-processing and various regularization techniques such as weight decay, dropout, and data augmentation. We also demonstrated how to use CNNs to classify MNIST and CIFAR-10.
In the next chapter, we'll build upon our new-found computer vision knowledge with some exciting additions. We'll discuss how to train networks faster by transferring knowledge from one problem to another, as well as the best performing advanced CNN architectures. We'll also go beyond simple classification with object detection, or how to find the object's location on the image. And for dessert, we'll talk about a fun CNN application called neural style...
