# wangshusen/DeepLearning

Repository: https://github.com/wangshusen/DeepLearning
Canonical: https://ross.abutalabs.com/products/wangshusen-deeplearning
Language: TeX
License: NOASSERTION
License Family: other
Last push: 2021-05-12T01:14:30+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": 2703, "days_push": 1940, "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 4221, forks 890 (observed 2026-08-28T04:08:39.296091+00:00)

## What it is
Course materials for CS583: Deep Learning, including lecture slides and notes in PDF/TeX covering machine learning basics, regression, classification, and neural networks. It is a free educational resource rather than software.

## Use cases
- learn deep learning fundamentals
- study backpropagation and multilayer perceptrons
- review machine learning basics like regression and classification
- find lecture slides for a university deep learning course
- learn Keras and deep learning libraries
- understand SVM, logistic regression, and clustering

## When to choose
- you want structured course-style materials for learning deep learning theory
- you need slides or lecture notes on classic ML and neural network topics
- you prefer free academic resources over paid courses

## When to avoid
- you need runnable code or a software library
- you want up-to-date content on modern LLMs and transformers
- you need interactive tutorials or exercises with automated grading

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: deep-learning, machine-learning, documentation
- domain: deep-learning, machine-learning, education, tutorials
- platform: cross-platform
- tags: course-materials, lecture-slides, university-course, tex, neural-networks

## Member repositories
- wangshusen/DeepLearning (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:39.296091+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 2026-08-29T18:22:24.437509+00:00, confidence not recorded.
  - readme: https://github.com/wangshusen/DeepLearning (fetched 2026-08-28T04:08:39.296091+00:00, sha 822ae375a9db)
- Data as of 2026-08-30T08:39:29.467469+00:00.