Official documentation is a strange thing to review as a course, because it is not trying to be one and yet an enormous number of people use it as their primary way of learning PyTorch. So the fair question is not whether it is a good course, which it is not, but whether it works for the job people actually put it to. The answer is partly, and it depends entirely on which door you come in through. Learn the Basics is the good door.
It is a short structured sequence covering tensors, datasets and dataloaders, transforms, building a model with nn.Module, autograd, the optimisation loop, and saving and loading. It takes a focused day. The writing is clear, the code is minimal, and when you finish you can read a PyTorch training script and know what every line is doing. For that specific outcome it is better than most paid content, and the fact that it is maintained by the people who maintain the library means it does not drift.
The sixty minute blitz sitting alongside it is similar and even quicker. If you already understand neural networks and just need to learn the framework, this is a genuinely efficient couple of days. The recipes section is the other thing I use regularly and rate highly. Short, single purpose pages that answer one question.
How do I save a checkpoint. How do I profile this. How do I use a learning rate scheduler. That format is exactly right for a working programmer and I would like more libraries to copy it.
The advanced material is where the site has no competition. Distributed data parallel, fully sharded data parallel, torch.compile and the compiler stack, custom C++ and CUDA extensions, quantisation, ExecuTorch for on device deployment. If you need to understand how to shard a model across eight GPUs, there is no course that covers it and this is where you will end up. It is written at the level of somebody who already knows what they are doing, which is appropriate for the audience, but it means the depth is real.
Now the problems, and the main one is structural. Beyond Learn the Basics there is no path. The tutorials index is a large grid organised by topic, and there is no indication of what order to do things in, what depends on what, or what you should have understood before starting any given page. For an experienced person that is fine, because you know what you are looking for.
For somebody trying to learn, it is a maze, and the common failure mode is jumping into a domain tutorial, following the code successfully, and coming away having learned nothing transferable because you never understood why any of it was structured that way. The variation in quality is the second problem. These tutorials come from many different contributors over many years and it shows. Some are excellent, carefully explained and current.
Others are thin walkthroughs of a script with comments, and a handful are visibly old, using idioms that have not been recommended for years. There is no quality signal on the index page, so you find out by starting one. The NLP tutorials in particular feel like relics, teaching character level RNNs and sequence to sequence models with attention as though the last several years did not happen. As history that is interesting.
As instruction in 2026 it is misleading about what people build. The third thing to be clear about is scope. This teaches you PyTorch. It does not teach you machine learning.
There is no treatment of when to use a given architecture, how to diagnose a model that is not learning, how to split data properly, what to do about class imbalance, or how to know whether your result means anything. Every tutorial trains successfully on a clean dataset and stops. Real work is mostly the parts that happen either side of that, and none of it appears here. Nor is there anything on experiment tracking, configuration management, reproducibility or the practical scaffolding of a project.
My 4.2 is for a resource that is free, official, accurate and unmatched at the advanced end, marked down for being a reference collection that a lot of people mistake for a curriculum. Use Learn the Basics as your framework introduction, learn the concepts from a book or a proper course, and come back here every time you need to do something specific. That is what it is good at and it is very good at it.