# maziarraissi/Applied-Deep-Learning

Applied Deep Learning Course

Repository: https://github.com/maziarraissi/Applied-Deep-Learning
Canonical: https://ross.abutalabs.com/products/applied-deep-learning
License Family: other
Last push: 2023-01-28T19:51:19+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": 1944, "days_push": 1313, "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 3558, forks 740 (observed 2026-08-28T04:08:09.725627+00:00)

## What it is
A two-semester graduate-level Applied Deep Learning course by Maziar Raissi, provided as lecture notes (PDFs) and YouTube playlists covering training deep neural networks, computer vision, and other state-of-the-art industry techniques. The repository hosts course materials rather than software, with assignments in Python using TensorFlow and PyTorch.

## Use cases
- learn applied deep learning from a structured course
- find lecture notes on training deep neural networks
- study computer vision topics like image classification and transfer learning
- supplement a graduate ML course with free video lectures
- get a curriculum covering industry deep learning practices

## When to choose
- you want a free, comprehensive deep learning curriculum with videos and notes
- you have a strong background in probability, statistics, linear algebra, and optimization
- you prefer theory-backed academic course material with Python/TensorFlow/PyTorch assignments

## When to avoid
- you need runnable software, a library, or production code
- you are a complete beginner without linear algebra and statistics background
- you need actively updated content, as the course materials date from 2023

## Facets
- artifact type: learning-resource
- maturity: stable
- function: deep-learning, machine-learning, computer-vision, nlp
- domain: deep-learning, machine-learning, tutorials, education
- platform: python
- tags: course, lecture-notes, youtube, tensorflow, pytorch, graduate-level

## Member repositories
- maziarraissi/Applied-Deep-Learning (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:09.725627+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 2026-08-29T18:34:14.901528+00:00, confidence not recorded.
  - readme: https://github.com/maziarraissi/Applied-Deep-Learning (fetched 2026-08-28T04:08:09.725627+00:00, sha 77548c62eff1)
- Data as of 2026-08-30T08:39:29.467469+00:00.