# patrickloeber/pytorchTutorial

PyTorch Tutorials from my YouTube channel

Repository: https://github.com/patrickloeber/pytorchTutorial
Canonical: https://ross.abutalabs.com/products/pytorchtutorial
Language: Python
License: MIT
License Family: permissive
Last push: 2023-11-06T16:19:03+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2447, "days_push": 1031, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1953, forks 1213 (observed 2026-08-28T04:05:58.836969+00:00)

## What it is
A collection of PyTorch beginner tutorial code examples accompanying Patrick Loeber's YouTube video series. It covers fundamentals from tensor basics and autograd through CNNs, transfer learning, and model saving.

## Use cases
- learn pytorch from scratch
- understand autograd and backpropagation
- build a first neural network in pytorch
- learn how to use dataloader and datasets
- implement cnn image classification in pytorch
- learn transfer learning in pytorch
- save and load trained pytorch models

## When to choose
- you are a beginner wanting structured, video-backed pytorch lessons
- you prefer learning by running small, self-contained example scripts
- you want a free curriculum covering core pytorch concepts end to end

## When to avoid
- you need production-ready or maintained library code
- you want advanced topics like distributed training or LLMs
- you need up-to-date examples for the latest pytorch APIs

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, deep-learning
- domain: deep-learning, machine-learning, tutorials
- platform: python
- tags: pytorch, tutorial, youtube, neural-networks, beginner

## Member repositories
- patrickloeber/pytorchTutorial (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:58.836969+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:06:33.950335+00:00, confidence not recorded.
  - readme: https://github.com/patrickloeber/pytorchTutorial (fetched 2026-08-28T04:05:58.836969+00:00, sha 81c59eec6131)
- Data as of 2026-08-30T08:39:29.467469+00:00.