# enggen/Deep-Learning-Coursera

Deep Learning Specialization by Andrew Ng, deeplearning.ai.

Repository: https://github.com/enggen/Deep-Learning-Coursera
Canonical: https://ross.abutalabs.com/products/enggen-deep-learning-coursera
Language: Jupyter Notebook
License Family: other
Last push: 2021-04-29T15:06:59+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": 3238, "days_push": 1952, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1758, forks 1315 (observed 2026-08-28T04:05:32.356261+00:00)

## What it is
A collection of Jupyter Notebook solutions and notes for Andrew Ng's Deep Learning Specialization on Coursera (deeplearning.ai). It covers neural networks, hyperparameter tuning, regularization, optimization, and ML project structuring.

## Use cases
- learn deep learning from scratch following Andrew Ng's course
- find reference solutions for Coursera deep learning assignments
- study neural network basics with worked notebooks
- review hyperparameter tuning and regularization techniques
- prepare for a career transition into AI
- supplement coursework with example implementations

## When to choose
- you are taking or reviewing the Deep Learning Specialization and want worked notebooks
- you prefer learning by reading and running Jupyter Notebook examples
- you want a free structured path into deep learning fundamentals

## When to avoid
- you need a production-ready deep learning library or framework
- you want maintained, up-to-date course materials (repo has no license and last release was 2021)
- you need advanced topics beyond the specialization's scope like transformers or LLMs

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: deep-learning, machine-learning, nlp, computer-vision
- domain: deep-learning, machine-learning, tutorials, artificial-intelligence
- platform: python, cross-platform
- tags: coursera, andrew-ng, jupyter-notebooks, coursework, educational

## Member repositories
- enggen/Deep-Learning-Coursera (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:32.356261+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 2026-08-30T03:27:34.311229+00:00, confidence not recorded.
  - readme: https://github.com/enggen/Deep-Learning-Coursera (fetched 2026-08-28T04:05:32.356261+00:00, sha 85f072e2ad5f)
- Data as of 2026-08-30T08:39:29.467469+00:00.