Summary
In this chapter, we explored CNNs, focusing on image data.
We started with an introduction to convolution layers, which motivates the name of this new family of neural networks. In this introduction, we explained why CNNs are so commonly used for image data, how convolutional networks work, and the impact of the many setting options. Next, we discussed pooling layers, commonly used in CNNs to efficiently downsample the data.
Finally, we put all this knowledge to work by building and training from scratch a CNN to classify images of digits between 0 and 9 from the MNIST dataset. Afterward, we discussed the concept of transfer learning, introduced four scenarios in which transfer learning can be applied, and showed how we can use transfer learning in the field of neural networks.
In the last section, we applied transfer learning to train a CNN to classify histopathology slide images. Instead of training it from scratch, this time we reused the convolutional layers of...
