# Atcold/NYU-DLSP21

NYU Deep Learning Spring 2021

Repository: https://github.com/Atcold/NYU-DLSP21
Canonical: https://ross.abutalabs.com/products/nyu-dlsp21
Homepage: https://atcold.github.io/NYU-DLSP21/
Language: Jupyter Notebook
License Family: other
Topics: deep-learning, yann-le-cunn, nyu, ebm
Last push: 2025-11-11T22:01:56+00:00

## Health v2 (maintenance only)
Score: 55/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 51, release rhythm 35, longevity 100
- inputs: {"age_days": 1966, "days_push": 295, "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 1675, forks 298 (observed 2026-08-28T04:05:20.499216+00:00)

## What it is
Course materials for NYU's Deep Learning (DS-GA 1008) Spring 2021 class taught by Yann LeCun and Alfredo Canziani, including slides, Jupyter notebooks, lecture transcriptions, and homework assignments. It covers backpropagation, CNNs and RNNs, energy-based models, transformers, graph networks, and control.

## Use cases
- learn deep learning fundamentals from Yann LeCun's NYU course
- study energy-based models with lecture notes and notebooks
- find Jupyter notebook practicums for backpropagation and gradient descent
- understand CNNs and RNNs through course slides and videos
- self-study a university-level deep learning curriculum
- learn about latent variable energy based models and autoencoders

## When to choose
- you want free, structured university course material on deep learning
- you prefer learning with a mix of videos, slides, and runnable notebooks
- you specifically want deep coverage of energy-based models

## When to avoid
- you need a software library or tool rather than educational content
- you want a course with graded assignments or certificates
- you need up-to-date material covering the latest LLM techniques

## Facets
- artifact type: learning-resource
- maturity: stable
- function: deep-learning, machine-learning
- domain: deep-learning, machine-learning, tutorials, education
- platform: python, cross-platform
- tags: nyu, yann-lecun, energy-based-models, jupyter-notebooks, course-material, pytorch, lectures

## Member repositories
- Atcold/NYU-DLSP21 (main) score 55

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:20.499216+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 2026-08-30T03:42:28.070406+00:00, confidence not recorded.
  - readme: https://github.com/Atcold/NYU-DLSP21 (fetched 2026-08-28T04:05:20.499216+00:00, sha c777697fe277)
  - homepage: https://atcold.github.io/NYU-DLSP21/ (fetched 2026-08-29T11:15:11.196224+00:00, sha 70ec147a89f9)
- Data as of 2026-08-30T08:39:29.467469+00:00.