In this chapter, we covered a lot of ground fast. We talked about convolutional layers and how they can be used for neural networks. We also covered batch normalization, pooling layers, and data augmentation. Finally, we trained a convolutional neural network from scratch using Keras and then improved that network using data augmentation.
We also talked about how data-hungry computer vision-based deep neural network problems are. In the next chapter I will show you transfer learning, which is one of my favorite techniques. It will help solve computer vision problems quickly, with amazing results and much less data.
