By now, you should be able to build and train three different types of model: linear, convolutional, and recurrent. You should have an appreciation of the theory and mathematics behind these model architectures and explain how they make predictions. Convolutional networks are probably the most studied deep learning network, especially in relation to image data. Of course, both convolutional and recurrent networks make extensive use of linear layers, so the theory behind linear networks, most notably linear regression and gradient descent, is fundamental to all artificial neural networks.
Our discussion so far has been fairly contained. We have looked at a well-studied problem, such as classification using MNIST, to give you a solid understanding of the basic PyTorch building blocks. This final chapter is the launching pad for your use of PyTorch...
