# kmario23/deep-learning-drizzle

Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!

Repository: https://github.com/kmario23/deep-learning-drizzle
Canonical: https://ross.abutalabs.com/products/deep-learning-drizzle
Homepage: https://deep-learning-drizzle.github.io
Language: HTML
License Family: other
Topics: machine-learning, deep-learning, deep-neural-networks, pattern-recognition, computer-vision, optimization, visual-recognition, reinforcement-learning, deep-reinforcement-learning, natural-language-processing, artificial-neural-networks, artificial-intelligence-algorithms, probabilistic-graphical-models, bayesian-statistics, speech-recognition, graph-neural-networks, medical-imaging, geometric-deep-learning, explainable-ai, probability
Last push: 2026-08-22T15:46:06+00:00

## Health v2 (maintenance only)
Score: 67/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 8, longevity 100
- inputs: {"age_days": 2838, "days_push": 11, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 12933, forks 2988 (observed 2026-08-28T04:11:00.169083+00:00)

## What it is
A curated collection of links to university lecture videos, slides, and course pages covering deep learning, machine learning, reinforcement learning, computer vision, NLP, and related topics. It is a static catalog (HTML page) pointing to free educational resources from institutions like Stanford, Oxford, and Toronto.

## Use cases
- find free deep learning lecture videos
- learn machine learning from university courses
- study reinforcement learning online
- find Stanford CS231n and CS224n course materials
- learn NLP and computer vision from lectures
- find Bayesian deep learning and GNN courses

## When to choose
- you want a curated index of free AI/ML lecture videos and courses
- you are self-studying deep learning fundamentals
- you want links to classic courses like CS231n, CS224n, and Hinton's neural networks lectures

## When to avoid
- you need interactive tutorials or hands-on coding exercises
- you need a structured curriculum with assessments or certificates
- you need up-to-date course content rather than links to lectures

## Facets
- artifact type: learning-resource
- maturity: stable
- function: machine-learning, deep-learning, reinforcement-learning, computer-vision, nlp, speech-recognition
- domain: deep-learning, machine-learning, artificial-intelligence, tutorials, computer-vision
- platform: -
- tags: awesome-list, lecture-videos, courses, curated-list, natural-language-processing, web

## Member repositories
- kmario23/deep-learning-drizzle (main) score 67

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:00.169083+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 2026-08-29T17:13:40.483187+00:00, confidence not recorded.
  - readme: https://github.com/kmario23/deep-learning-drizzle (fetched 2026-08-28T04:11:00.169083+00:00, sha de970e3f3e7e)
  - homepage: https://deep-learning-drizzle.github.io (fetched 2026-08-29T08:09:35.231797+00:00, sha 80be170430fb)
- Data as of 2026-08-30T08:39:29.467469+00:00.