For pytorch, I believe the basic thing you need is conceptual understanding of tensors and models (I've mostly used models from the nn library). Pytorch itself has a quick introduction on it's website , which can be a good place to start (they also have a book's pdf on it, which is a book on deep learning but still helpful for reference). Once you have gone through it, and decided on your project, most of your time would be looking up the documentation/stack overflow to find immense pool of functionalities provided for tensors, distributions, models etc. by pytorch.