# stephencwelch/Neural-Networks-Demystified

Supporting code for short YouTube series Neural Networks Demystified.

Repository: https://github.com/stephencwelch/Neural-Networks-Demystified
Canonical: https://ross.abutalabs.com/products/neural-networks-demystified
Language: Jupyter Notebook
License Family: other
Last push: 2023-08-19T11:35:17+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": 4260, "days_push": 1110, "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 1362, forks 571 (observed 2026-08-28T04:04:30.219503+00:00)

## What it is
Supporting Jupyter notebooks and Python scripts for the 'Neural Networks Demystified' YouTube series, teaching neural network fundamentals from scratch. It combines formulas, code, and explanatory text in runnable notebooks.

## Use cases
- learn how neural networks work from scratch
- follow along with a neural network video tutorial in code
- implement backpropagation by hand in python
- find beginner-friendly neural network notebooks
- understand gradient descent with worked examples

## When to choose
- you are a beginner wanting an intuitive, code-first introduction to neural networks
- you want to pair video lessons with runnable notebooks
- you prefer minimal dependencies and from-scratch implementations

## When to avoid
- you need a production-ready deep learning framework
- you want modern tooling, GPU support, or maintained code (Python 2 era)
- you need a permissive license for reuse

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, deep-learning
- domain: machine-learning, deep-learning, tutorials, education
- platform: python, cross-platform
- tags: jupyter-notebooks, neural-networks, video-series, educational

## Member repositories
- stephencwelch/Neural-Networks-Demystified (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:30.219503+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 2026-08-30T04:41:32.045486+00:00, confidence not recorded.
  - readme: https://github.com/stephencwelch/Neural-Networks-Demystified (fetched 2026-08-28T04:04:30.219503+00:00, sha c52287fab723)
- Data as of 2026-08-30T08:39:29.467469+00:00.