# rasbt/deeplearning-models

A collection of various deep learning architectures, models, and tips

Repository: https://github.com/rasbt/deeplearning-models
Canonical: https://ross.abutalabs.com/products/deeplearning-models
Language: Jupyter Notebook
License: MIT
License Family: permissive
Last push: 2024-02-08T16:35:25+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2646, "days_push": 937, "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 17588, forks 4107 (observed 2026-08-28T04:11:20.017244+00:00)

## What it is
A curated collection of deep learning architectures, models, and tips implemented as Jupyter Notebooks in both PyTorch and TensorFlow. It covers everything from basic machine learning algorithms like perceptrons to CNNs and other neural network architectures, with many examples provided in multiple framework variants.

## Use cases
- learn how to implement neural networks from scratch
- compare PyTorch and TensorFlow implementations of the same model
- find reference code for CNN architectures
- study deep learning tips like dropout and batch normalization
- get started with PyTorch Lightning examples
- understand backpropagation with a from-scratch implementation

## When to choose
- you want side-by-side PyTorch and TensorFlow code for the same architecture
- you are learning deep learning and prefer runnable notebooks
- you need clean, educational reference implementations rather than production libraries

## When to avoid
- you need a production-ready model library or pip-installable package
- you want the latest state-of-the-art architectures
- you need maintained, actively updated code for current framework versions

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: deep-learning, machine-learning, developer-tools
- domain: deep-learning, machine-learning, tutorials, computer-vision
- platform: python, cross-platform
- tags: jupyter-notebooks, pytorch, tensorflow, pytorch-lightning, educational, neural-networks, model-architectures, natural-language-processing

## Member repositories
- rasbt/deeplearning-models (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:20.017244+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 2026-08-29T17:02:52.973255+00:00, confidence not recorded.
  - readme: https://github.com/rasbt/deeplearning-models (fetched 2026-08-28T04:11:20.017244+00:00, sha d338b5d7e71d)
- Data as of 2026-08-30T08:39:29.467469+00:00.