OtherTen modules, around 25 hours of video, realistically a few months if you code along properly·Free online book at learnpytorch.io and a full course on YouTube, paid video version via a Zero to Mastery subscription

Learn PyTorch for Deep Learning (Daniel Bourke)

4.6

The course to do if you want to actually write PyTorch rather than nod along to someone else writing it. Daniel Bourke's motto is basically if in doubt, run the code, and the whole thing is built around that, which is exactly why it works so much better than the average deep learning tutorial.

What We Liked

Free to read in full as a polished online book, with the video also on YouTube at no cost
Relentlessly hands on, every concept comes with runnable code you are expected to type and break
Clear, sequential structure across ten modules that takes you from tensors to a deployed model
Friendly, encouraging teaching style that keeps a long course from feeling like a slog

What Could Be Better

PyTorch specific by design, so you will not learn TensorFlow or a framework agnostic view here
Deliberately light on heavy mathematical theory, favouring build first and understand as you go
The sheer length can be intimidating, and it genuinely takes months to do well
As with any fast moving library, you will occasionally hit small version differences to work around

Detailed review

Deep learning has a peculiar failure mode where you can watch hours of lectures, feel like you understand everything, and then freeze the moment you open an empty editor. Daniel Bourke's Learn PyTorch for Deep Learning is built specifically to prevent that. The entire course is code first. You are not shown a finished model and asked to admire it, you are walked through writing it, running it, watching it fail, and fixing it, which is how anyone actually learns to build.

The material exists in a few forms that all point at the same content: a genuinely lovely free online book at learnpytorch.io, a long form video on the freeCodeCamp YouTube channel, and a paid video version bundled into the Zero to Mastery subscription for people who prefer that format and community. Across roughly ten modules it moves from the basics of tensors and how PyTorch thinks about data, into building neural networks, training loops, evaluation, computer vision, custom datasets, and eventually taking a model and deploying it, so by the end you have touched the full arc rather than just the fun modelling bit in the middle. The teaching voice is warm and patient, which matters more than it sounds on a course this long, because the thing that kills most self taught learners is quietly giving up around the halfway point. The trade offs are honest ones.

This is a PyTorch course and only a PyTorch course, so if your world runs on TensorFlow you are in the wrong room, and it intentionally goes light on the deeper mathematics, trusting that building working intuition first will motivate the theory later. That is a defensible choice and the right one for most people, but if you want rigorous proofs you will need to look elsewhere. The length is also real, and calling it a course you finish in a weekend would be dishonest, it is a commitment measured in months if you do it properly. My advice is simple and it is the same as the author's: run the code.

Do not passively watch. Type it, change the numbers, deliberately break it and understand why it broke, and you will walk away actually able to build in PyTorch, which is a rarer outcome from a free course than it should be.

[ final ]

The verdict.

If you know a little Python and want to come out the other side genuinely able to build things in PyTorch, this is one of the best options at any price, and it happens to be free. Do not just watch it. Type every example, break things on purpose, and you will finish with real, transferable skill rather than the illusion of it.