Previously, in Chapter 11, Deep and Wide Neural Networks, we used a dataset that was very challenging for a general-purpose network. However, convolutional neural networks (CNNs) will prove to be more effective, as you will see. CNNs have been around since the late 80s (LeCun, Y., et al. (1989)). They have transformed the world of computer vision and audio processing (Li, Y. D., et al. (2016)). If you have some kind of AI-based object recognition capability in your smartphone, chances are it is using some kind of CNN architecture; for example:
- The recognition of objects in images
- The recognition of a digital fingerprint
- The recognition of voice commands
CNNs are interesting because they have solved some of the most challenging problems in computer vision, including beating a human being at an image recognition problem called ImageNet (Krizhevsky, A., et al. (2012)). If you can think of the most complex object recognition tasks, CNNs should...
