Throughout this book, we have learned about various types of neural networks that are used in deep learning, such as convolutional neural networks and recurrent neural networks, and they have achieved some tremendous results in a variety of tasks, such as computer vision, image reconstruction, synthetic data generation, speech recognition, language translation, and so on. All of the models we have looked at so far have been trained on Euclidean data, that is, data that can be represented in grid (matrix) format—images, text, audio, and so on.
However, many of the tasks that we would like to apply deep learning to use non-Euclidean data (more on this shortly) – the kind that the neural networks we have come across so far are unable to process and deal with. This includes dealing with sensor networks, mesh surfaces, point clouds, objects (the...
