# udacity/deep-learning-v2-pytorch

Projects and exercises for the latest Deep Learning ND program https://www.udacity.com/course/deep-learning-nanodegree--nd101

Repository: https://github.com/udacity/deep-learning-v2-pytorch
Canonical: https://ross.abutalabs.com/products/deep-learning-v2-pytorch
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: deep-learning, neural-network, convolutional-networks, pytorch, recurrent-networks, style-transfer, sentiment-analysis
Last push: 2023-06-27T01:54:55+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": 2920, "days_push": 1164, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 5518, forks 5318 (observed 2026-08-28T04:09:21.865126+00:00)

## What it is
A collection of Jupyter Notebook tutorials and projects from Udacity's Deep Learning Nanodegree program, implemented in PyTorch. It covers neural networks, CNNs, RNNs, GANs, transfer learning, style transfer, and sentiment analysis through hands-on exercises.

## Use cases
- learn deep learning with pytorch
- tutorial notebooks for convolutional neural networks
- implement a sentiment analysis model from scratch
- learn style transfer with pretrained networks
- practice building GANs and autoencoders
- understand transfer learning with VGG
- study materials for a deep learning nanodegree

## When to choose
- you want hands-on, notebook-based deep learning tutorials in PyTorch
- you are a beginner learning neural networks, CNNs, and RNNs step by step
- you want guided projects like style transfer, sentiment analysis, or GANs

## When to avoid
- you need a production-ready deep learning library or framework
- you want up-to-date course content or active community support
- you prefer TensorFlow or non-PyTorch tooling

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: deep-learning, machine-learning, nlp, image-processing
- domain: deep-learning, machine-learning, tutorials, computer-vision
- platform: python, cross-platform
- tags: pytorch, jupyter-notebooks, neural-networks, cnn, rnn, gan, style-transfer, sentiment-analysis, transfer-learning, nanodegree, educational, natural-language-processing

## Member repositories
- udacity/deep-learning-v2-pytorch (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:21.865126+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 2026-08-29T17:55:47.903104+00:00, confidence not recorded.
  - readme: https://github.com/udacity/deep-learning-v2-pytorch (fetched 2026-08-28T04:09:21.865126+00:00, sha a5c5841a3d39)
- Data as of 2026-08-30T08:39:29.467469+00:00.