What it does This repo houses the complete materials for the Zero to Mastery “Learn PyTorch for Deep Learning” course: 10 sections of notebooks, slides, exercises, and solutions, all readable as a free online book at learnpytorch.io. The content runs from tensor fundamentals through computer vision, transfer learning, experiment tracking, paper replication, and finally deploying a model to the internet. Everything is designed to run in Google Colab, so you don’t need a GPU sitting under your desk.

The interesting bit The author cheerfully calls this “the second best place to learn PyTorch on the internet” — the first being PyTorch’s own documentation. That honesty is refreshing, and it signals something useful: this is structured curriculum, not a replacement for docs. The whole arc builds toward FoodVision, a running computer-vision project where you classify food images, which gives the scattered topics a narrative backbone.

Key highlights

• 321 videos and 10 complete sections, all skeleton code, annotations, images, and exercises marked done
• Heavy emphasis on “code, code, code, experiment, experiment, experiment” — the author’s words, not mine
• Includes a dedicated PyTorch 2.0 tutorial; previous materials remain compatible because 2.0 is additive
• Three milestone projects: experiment tracking, paper replication, and model deployment
• Apprenticeship-style teaching: instructor writes code, you write code alongside

Caveats

• Explicitly beginner-targeted; the author warns that 1+ years of ML experience means this may move slowly
• Requires 3–6 months of Python and some prior ML exposure (or willingness to catch up via linked resources)
• Video certificates exist, but the author dismisses them as “meh” — the value is in the code written, not the credential

Verdict Grab this if you’re early in your ML journey and want a guided, notebook-heavy path through PyTorch with homework and solutions. Skip it if you’re already comfortable reading research implementations or need advanced topics like NLP or time series in depth — those get only resource links, not full treatment.