Hands-On Java Deep Learning for Computer Vision

Summary

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 ...