My comprehensive PyTorch course is now live on the freeCodeCamp.org The best way to learn is by doing. And that's just what we'll do in the Learn PyTorch for Deep Learning: Zero to Mastery course. If you're new to data science and machine learning, consider the course a momentum builder. By the end, you'll be comfortable navigating the PyTorch documentation, reading PyTorch code, writing PyTorch code, searching for things you don't understand and building your own machine learning projects.

Model training has been and will be in the foreseeable future one of the most frustrating things machine learning developers face. It takes quite a long time and people can't really do anything about it. If you have the luxury (especially at this moment of time) of having multiple GPUs, you are likely to find Distributed Data Parallel (DDP) helpful in terms of model training. DDP performs model training across multiple GPUs, in a transparent fashion. You can have multiple GPUs on a single machine, or multiple machines separately.

There's no universally best way to learn about machine learning. But one of my most common techniques is to find a code example of whatever new topic I'm interested in, get the example to run, then refactor the working example to a simpler form. Refactoring code forces me to examine every line of code and make sure I understand it (well, for the most part anyway). I applied this learning strategy to variational autoencoders using the PyTorch neural library. The documentation demo creates a variational autoencoder that generates synthetic MNIST digits from '0' to '9'.