We hope you enjoyed learning about edge detection, and creating an application to detect the edges of complex images using different types of filters. We took an in-depth look at convolution and worked with its layers, which helped us to understand complex convolution neural networks. We saw the benefit of pooling layers in building a CNNs—they reduce the number of parameters drastically. We saw why convolution is the ultimate technique for achieving better accuracy and proved it by building and training a CNN that showed how the accuracy percentage was improving consistently over time rather than sticking at 97%, as with the simple neural networks.
In the next chapter, we'll look at transfer learning and the deep convolution neural network architecture, which will enable us to achieve state-of-the-art accuracy.
