Summary
In this module, you have learned how convolutional neural networks work, and how they can capture patterns in 2D images. In fact, CNNs can also be used for finding patterns in 1-dimensional signals (such as sound waves, or time series), and in multi-dimensional structures (for example, events in videos, where some patterns are repeated across frames).
Also, CNNs are simple building blocks for solving more complex computer vision tasks, such as Image Generation. Generative Adversarial Networks can be used to generate images similar to the ones in the given dataset, for example, they can be used to produce computer-generated paintings. Similarly, CNNs are used for object detection, instance segmentation, etc. Learning how to implement neural networks to solve those problems is the subject of a separate course, and we suggest that you continue your journey of mastering computer vision!