# rasbt/machine-learning-book

Code Repository for Machine Learning with PyTorch and Scikit-Learn

Repository: https://github.com/rasbt/machine-learning-book
Canonical: https://ross.abutalabs.com/products/machine-learning-book
Homepage: https://sebastianraschka.com/books/#machine-learning-with-pytorch-and-scikit-learn
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: machine-learning, scikit-learn, deep-learning, neural-networks, pytorch
Last push: 2026-06-15T14:33:04+00:00

## Health v2 (maintenance only)
Score: 62/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 87, release rhythm 8, longevity 100
- inputs: {"age_days": 1719, "days_push": 79, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 5345, forks 1835 (observed 2026-08-28T04:09:15.888786+00:00)

## What it is
The official code repository for the book 'Machine Learning with PyTorch and Scikit-Learn' by Sebastian Raschka, Yuxi Liu, and Vahid Mirjalili. It contains Jupyter notebooks with code examples covering classical machine learning, deep learning, transformers, GANs, graph neural networks, and reinforcement learning.

## Use cases
- learn machine learning with pytorch and scikit-learn
- jupyter notebooks for deep learning book
- implement a neural network from scratch
- learn transformers and attention mechanisms
- study reinforcement learning examples
- learn convolutional neural networks for image classification
- practice model evaluation and hyperparameter tuning

## When to choose
- you are reading the book and want its companion code
- you want hands-on, chapter-by-chapter ML and deep learning examples in PyTorch
- you prefer learning via runnable Jupyter notebooks

## When to avoid
- you need a production-ready ML library rather than educational code
- you want standalone tutorials without the book's explanatory text
- you need a framework for deploying models to production

## Facets
- artifact type: learning-resource
- maturity: stable
- function: machine-learning, deep-learning, nlp, computer-vision, reinforcement-learning
- domain: machine-learning, deep-learning, tutorials, education
- platform: python, cross-platform
- tags: pytorch, scikit-learn, jupyter-notebooks, book-companion, neural-networks, transformers, gan, graph-neural-networks, natural-language-processing

## Member repositories
- rasbt/machine-learning-book (main) score 62

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:15.888786+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 2026-08-29T17:58:48.861557+00:00, confidence not recorded.
  - readme: https://github.com/rasbt/machine-learning-book (fetched 2026-08-28T04:09:15.888786+00:00, sha daaf29e837a3)
  - homepage: https://sebastianraschka.com/books/#machine-learning-with-pytorch-and-scikit-learn (fetched 2026-08-29T08:53:14.834395+00:00, sha 165b2fa98eed)
  - site_page: https://sebastianraschka.com/about (fetched 2026-08-29T08:53:14.844383+00:00, sha 667cdfd072dc)
- Data as of 2026-08-30T08:39:29.467469+00:00.