# dvgodoy/PyTorchStepByStep

Official repository of my book: "Deep Learning with PyTorch Step-by-Step: A Beginner's Guide"

Repository: https://github.com/dvgodoy/PyTorchStepByStep
Canonical: https://ross.abutalabs.com/products/pytorchstepbystep
Homepage: https://pytorchstepbystep.com
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: deep-learning, pytorch, pytorch-tutorial, python, cnn-pytorch, rnn-pytorch
Last push: 2026-02-19T21:12:52+00:00

## Health v2 (maintenance only)
Score: 63/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 68, release rhythm 35, longevity 100
- inputs: {"age_days": 2332, "days_push": 195, "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 1484, forks 508 (observed 2026-08-28T04:04:51.348881+00:00)

## What it is
The official companion repository for the book 'Deep Learning with PyTorch Step-by-Step: A Beginner's Guide', containing one runnable Jupyter notebook per chapter. It teaches PyTorch from first principles, covering gradient descent through CNNs, RNNs, and fine-tuning NLP models with HuggingFace.

## Use cases
- learn pytorch from scratch
- understand how gradient descent and training loops work
- learn convolutional neural networks in pytorch
- learn rnn lstm and seq2seq models
- fine-tune bert and gpt-2 with huggingface
- run pytorch tutorials in google colab with free gpu
- beginner-friendly deep learning book with runnable code

## When to choose
- you want a structured, incremental, from-first-principles introduction to PyTorch
- you prefer conversational, plain-English explanations over heavy math notation
- you want reproducible notebooks matching book outputs
- you are a beginner starting deep learning with PyTorch 2.x

## When to avoid
- you need a production PyTorch library or framework rather than learning material
- you want a quick image-classification-only tutorial without fundamentals
- you need a formal, mathematically rigorous textbook treatment

## Facets
- artifact type: learning-resource
- maturity: active
- function: deep-learning, machine-learning, nlp, computer-vision
- domain: deep-learning, machine-learning, tutorials, computer-vision
- platform: python, browser
- tags: pytorch, jupyter-notebooks, book-companion, beginner-friendly, gradient-descent, cnn, rnn, transformers, huggingface, google-colab, natural-language-processing, gpu

## Member repositories
- dvgodoy/PyTorchStepByStep (main) score 63

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:51.348881+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 2026-08-30T04:34:00.234804+00:00, confidence not recorded.
  - readme: https://github.com/dvgodoy/PyTorchStepByStep (fetched 2026-08-28T04:04:51.348881+00:00, sha 1cd98e78278b)
  - homepage: https://pytorchstepbystep.com (fetched 2026-08-29T11:40:20.332473+00:00, sha 9e7c24fe307e)
- Data as of 2026-08-30T08:39:29.467469+00:00.