# priya-dwivedi/Deep-Learning

Repository: https://github.com/priya-dwivedi/Deep-Learning
Canonical: https://ross.abutalabs.com/products/priya-dwivedi-deep-learning
Language: Jupyter Notebook
License: MIT
License Family: permissive
Last push: 2023-03-24T22:23:11+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": 3668, "days_push": 1258, "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 3355, forks 2438 (observed 2026-08-28T04:07:57.928120+00:00)

## What it is
A collection of standalone deep learning projects in Jupyter Notebooks, each in its own folder, covering topics the author explored and blogged about on Medium. It serves as a portfolio and learning resource rather than a reusable library.

## Use cases
- learn deep learning through worked examples
- find jupyter notebook projects on computer vision and nlp
- see end-to-end deep learning project examples
- study implementations of interesting ML ideas
- get inspiration for personal deep learning projects

## When to choose
- you want runnable notebook examples of diverse deep learning projects
- you are learning and want to clone and experiment with complete projects
- you want code accompanying the author's Medium blog posts

## When to avoid
- you need a production-ready library or package with an API
- you need actively maintained, tested software
- you need a single coherent framework rather than independent projects

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: deep-learning, machine-learning, data-science, computer-vision, nlp
- domain: deep-learning, machine-learning, tutorials, data-science
- platform: python
- tags: jupyter-notebooks, example-projects, portfolio, tutorial-collection

## Member repositories
- priya-dwivedi/Deep-Learning (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:57.928120+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 2026-08-29T18:40:55.836635+00:00, confidence not recorded.
  - readme: https://github.com/priya-dwivedi/Deep-Learning (fetched 2026-08-28T04:07:57.928120+00:00, sha d112aabec46f)
- Data as of 2026-08-30T08:39:29.467469+00:00.