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PyTorch for Deep Learning Bootcamp

4.6(18,000) on Udemy·210K enrolled
Intermediate 17 hours English Bootcamp
SkillsPyTorchDeep learningNeural networksComputer visionTensorsModel building

Is this course right for you?

Our take
A hands-on way to learn deep learning by building in PyTorch, once you already know Python. It is project-driven with production-quality code.

Good for: python programmers who want a hands-on route into deep learning with PyTorch.

Skip if: you are new to Python, or you want deep mathematical theory.

Daniel Bourke covers PyTorch from tensors, autograd and nn.Module through CNNs for vision, recurrent architectures and transfer learning, mirroring his widely used GitHub repo so you finish with a real portfolio of trained models and clean, reusable code.

What it does not do is teach Python or the deep mathematical theory — it assumes Python and is build-first — so if you are new to Python, or you want the maths, start elsewhere instead. PyTorch leads in research and is widely used; Bourke keeps it updated, and on price, wait for a Udemy sale.

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About this course

This bootcamp covers PyTorch from the ground up: tensors, autograd, and nn.Module, then advances to CNNs for image classification, recurrent architectures, and transfer learning with pretrained models. Every section is project-driven with production-quality code.

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Instructor

DB
Daniel Bourke
Udemy instructor
210K+ learners20 courses4.6 instructor rating

Daniel Bourke is a machine learning engineer and educator known for practical, project-first teaching. His PyTorch tutorial repository has tens of thousands of GitHub stars.

Frequently asked questions

Yes, and a fair amount. This bootcamp recommends roughly three to six months of Python experience, plus comfort with Jupyter notebooks or Google Colab, because it moves quickly into representing data as tensors and building models. It is not a place to learn Python itself. If you are starting from zero, Daniel Bourke's machine-learning or Python courses (bundled in the ZTM membership) are the better first step.

PyTorch is the easier framework to learn and dominates research and experimentation, with a readable, dynamic style and strong beginner community — which is why this course teaches it. TensorFlow still leads for some production and mobile or edge deployment. If you are starting out or heading toward research, PyTorch is the more natural first choice; many engineers eventually learn both, but you do not need to begin with both.

A hands-on path through PyTorch: fundamentals and tensors, the core training workflow, neural-network classification, computer vision, and working with custom datasets, across roughly fifty hours and several milestone projects. Everything is coded line-by-line alongside the instructor rather than lectured abstractly, so you build real models throughout — the format that actually makes deep-learning concepts stick.

For someone with the Python prerequisite wanting a genuinely practical, build-everything introduction to deep learning with PyTorch, it is one of the more recommended courses, taught by a working machine-learning engineer. The value comes from coding along and then extending the projects yourself. It builds strong applied foundations; going deeper into a specialism afterward is what turns that into job-level capability.

It is available on Udemy, usually at a low price during frequent sales, and through the Zero To Mastery subscription which bundles it with the prerequisite Python and machine-learning courses. Much of the same material is also on Daniel Bourke's free YouTube version. Either way you get lifetime or subscription access, so most people find it strong value for the depth on offer.
Paid
Paid, frequently discounted
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