# Kulbear/deep-learning-coursera

Deep Learning Specialization by Andrew Ng on Coursera.

Repository: https://github.com/Kulbear/deep-learning-coursera
Canonical: https://ross.abutalabs.com/products/deep-learning-coursera
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: deep-learning, coursera
Archived: true
Last push: 2019-05-22T09:25:59+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3311, "days_push": 2660, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, archived
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 7735, forks 5459 (observed 2026-08-28T04:10:02.674918+00:00)

## What it is
A collection of completed programming assignments, quiz solutions, and notes for Andrew Ng's Deep Learning Specialization on Coursera, written as Jupyter Notebooks. It serves as a reference for learners working through the five-course specialization covering neural networks, hyperparameter tuning, and structured machine learning projects.

## Use cases
- reference solutions for deep learning coursera assignments
- learn neural networks from scratch with python notebooks
- understand logistic regression as a neural network mindset
- study hyperparameter tuning and regularization examples
- prepare for the deep learning specialization quizzes
- find worked examples of building deep neural networks step by step

## When to choose
- you are enrolled in or considering Andrew Ng's Deep Learning Specialization and want reference material
- you learn best from worked Jupyter Notebook examples with step-by-step implementations
- you want to check your own assignment solutions against a completed set

## When to avoid
- you want a production deep learning library or framework
- you intend to copy assignment solutions rather than learn from them, which the author explicitly discourages
- you need up-to-date course content matching the current Coursera version, as the repo was last updated in 2019

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: deep-learning, machine-learning
- domain: deep-learning, machine-learning, tutorials
- platform: python
- tags: coursera, andrew-ng, jupyter-notebooks, course-assignments, educational

## Member repositories
- Kulbear/deep-learning-coursera (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:02.674918+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 2026-08-29T17:35:11.783983+00:00, confidence not recorded.
  - readme: https://github.com/Kulbear/deep-learning-coursera (fetched 2026-08-28T04:10:02.674918+00:00, sha 43ec91a1c756)
- Data as of 2026-08-30T08:39:29.467469+00:00.