Learn PyTorch: The best free online courses and tutorials

Deep learning continues to be one of the hottest fields in computing, and while Google’s TensorFlow remains the most popular framework in absolute numbers, Facebook’s PyTorch has quickly earned a reputation for being easier to grasp and use.

PyTorch has taken the world of deep learning research by storm, outstripping TensorFlow as the implementation framework of choice in submitted papers for AI conferences in the past two years. With recent improvements for producing optimized models and deploying them to production, PyTorch is definitely a framework ready for use in industry as well as R&D labs.

But how to get started? You’ll find plenty of books and paid resources available for learning PyTorch, of course. But there are also plenty of resources on the Internet that will help you get to grips with the framework — for absolutely nothing. Plus, some of the free resources are of even higher quality than what you can pay for. Let’s take a look at what is on offer.

PyTorch.org tutorials

Perhaps the most obvious place to start is the PyTorch website itself. Along with the usual resources such as an API reference, the website includes more digestible works such as a 60-minute video and text blitz through PyTorch via setting up an image classification model. There are guides for both the standard and the more esoteric features of the framework, and when a new major capability is added, such as quantization or pruning of models, you’ll normally get a quick tutorial on how to implement them in your own applications.

On the downside, the code in the various tutorials tends to vary quite a lot, and sometimes standard steps will be missed or passed over in order to show off the feature that the tutorial is concentrating on rather than producing idiomatic PyTorch code. In fairness, the tutorial code has definitely improved over the past couple of years, but you do sometimes have to be a little careful. For this reason, I wouldn’t recommend using the PyTorch website as your primary resource for learning. Nevertheless, it’s a useful resource to have on hand — and the best place to learn how to use the latest new features.

Udacity’s and edX’s PyTorch deep learning courses

I’m bundling Udacity’s Introduction to Deep Learning with PyTorch and edX’s Deep Learning with Python and PyTorch together here as they have similar structures, cover a lot of the same ground, and appear to suffer from the same issues. They both have a traditional series of lectures that build up from the foundations of deep learning, introducing you to concept after concept, then tackling more complex scenarios such as image and text classification by the end of the course. This is a completely fine way to go about teaching deep learning, but it does mean that you’ll be sinking some considerable time into the lessons before you get to do anything exciting with PyTorch, unlike, say, what happens with the Fast.ai course.

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