# Spandan-Madan/DeepLearningProject

An in-depth machine learning tutorial introducing readers to a whole machine learning pipeline from scratch.

Repository: https://github.com/Spandan-Madan/DeepLearningProject
Canonical: https://ross.abutalabs.com/products/deeplearningproject
Homepage: https://spandan-madan.github.io/DeepLearningProject/
Language: HTML
License: MIT
License Family: permissive
Topics: machine-learning, deep-learning, neural-networks, tutorial
Last push: 2023-01-15T06:31:21+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 3339, "days_push": 1326, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4814, forks 640 (observed 2026-08-28T04:09:00.318334+00:00)

## What it is
An in-depth end-to-end tutorial of a machine learning pipeline, originally built for a Harvard data science class, covering dataset creation, conventional ML, and deep learning. It is available as an HTML page and Jupyter notebooks, with both TensorFlow/Keras and PyTorch versions.

## Use cases
- learn the full machine learning pipeline from scratch
- understand how to build your own dataset instead of using MNIST
- compare conventional ML vs deep learning approaches
- learn deep learning with PyTorch through a worked example
- find a weekend-length ML tutorial beyond quick syntax intros
- study model evaluation and implementation decisions in ML

## When to choose
- you want a comprehensive, end-to-end walkthrough of a real ML pipeline
- you are a student or practitioner moving beyond toy MNIST tutorials
- you want to learn how to create and use your own dataset
- you prefer learning via Jupyter notebooks with runnable code

## When to avoid
- you need a quick 30-minute introduction to neural networks
- you need production-ready or maintained ML code
- you require support for modern Python versions, as the original targets Python 2.7
- you want cutting-edge deep learning techniques

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, deep-learning, data-science
- domain: machine-learning, deep-learning, data-science, tutorials
- platform: python, cross-platform
- tags: tutorial, jupyter-notebook, pytorch, tensorflow, machine-learning-pipeline, harvard, educational

## Member repositories
- Spandan-Madan/DeepLearningProject (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:00.318334+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 2026-08-29T18:18:34.794894+00:00, confidence not recorded.
  - readme: https://github.com/Spandan-Madan/DeepLearningProject (fetched 2026-08-28T04:09:00.318334+00:00, sha 380372b6b52b)
  - homepage: https://spandan-madan.github.io/DeepLearningProject/ (fetched 2026-08-29T09:01:30.965241+00:00, sha 646ed0238c95)
- Data as of 2026-08-30T08:39:29.467469+00:00.