# makeyourownneuralnetwork/makeyourownneuralnetwork

Code for the Make Your Own Neural Network book

Repository: https://github.com/makeyourownneuralnetwork/makeyourownneuralnetwork
Canonical: https://ross.abutalabs.com/products/makeyourownneuralnetwork
Language: Jupyter Notebook
License: GPL-2.0
License Family: copyleft
Last push: 2024-01-24T17:50:15+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": 3845, "days_push": 952, "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 2496, forks 1503 (observed 2026-08-28T04:06:56.693447+00:00)

## What it is
Companion code repository for the book 'Make Your Own Neural Network', containing Jupyter notebooks that build a simple three-layer neural network from scratch in Python. It is an educational resource for learning how neural networks work at a fundamental level.

## Use cases
- learn how neural networks work from scratch
- implement a simple neural network in pure Python and numpy
- follow along with the Make Your Own Neural Network book exercises
- teach beginners the math behind backpropagation
- train a small network to recognize handwritten digits (MNIST)
- understand neural network basics without using deep learning frameworks

## When to choose
- you want to understand neural networks conceptually rather than just use a framework
- you are a beginner learning machine learning fundamentals
- you prefer minimal, readable code with no heavy dependencies
- you are reading the book and want runnable companion notebooks

## When to avoid
- you need production-grade or GPU-accelerated deep learning
- you want modern architectures like CNNs or transformers
- you need a maintained library with active feature development
- you already know neural network fundamentals and need advanced tooling

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, deep-learning, data-science
- domain: machine-learning, deep-learning, education, tutorials
- platform: python
- tags: neural-networks, book-companion-code, jupyter-notebook, beginner-friendly, numpy

## Member repositories
- makeyourownneuralnetwork/makeyourownneuralnetwork (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:56.693447+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 2026-08-30T02:27:11.891154+00:00, confidence not recorded.
  - readme: https://github.com/makeyourownneuralnetwork/makeyourownneuralnetwork (fetched 2026-08-28T04:06:56.693447+00:00, sha e93e503d2273)
- Data as of 2026-08-30T08:39:29.467469+00:00.