# amanchadha/coursera-deep-learning-specialization

Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models

Repository: https://github.com/amanchadha/coursera-deep-learning-specialization
Canonical: https://ross.abutalabs.com/products/coursera-deep-learning-specialization
Language: Jupyter Notebook
License Family: other
Topics: deep-learning, coursera, coursera-assignment, coursera-specialization, coursera-machine-learning, andrew-ng, andrew-ng-course, convolutional-neural-networks, cnns, recurrent-neural-networks, rnns, convolutional-neural-network, recurrent-neural-network, hyperparameter-optimization, hyperparameter-tuning, neural-network, neural-networks, neural-machine-translation, neural-style-transfer, regularization
Last push: 2026-06-10T22:37:15+00:00

## Health v2 (maintenance only)
Score: 71/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 86, release rhythm 35, longevity 100
- inputs: {"age_days": 2261, "days_push": 84, "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 4352, forks 2676 (observed 2026-08-28T04:08:46.341520+00:00)

## What it is
A collection of notes, programming assignments, and quizzes from all five courses of the Coursera Deep Learning Specialization by deeplearning.ai (Andrew Ng). It includes Jupyter Notebook solutions covering neural networks, hyperparameter tuning, CNNs, and sequence models, updated to TensorFlow 2.

## Use cases
- study for the Coursera Deep Learning specialization
- find reference solutions for deep learning programming assignments
- learn neural networks with worked Jupyter notebooks
- review interview-ready deep learning notes
- practice hyperparameter tuning and regularization exercises
- learn CNNs and RNNs with hands-on assignments

## When to choose
- you are enrolled in or self-studying the deeplearning.ai Deep Learning Specialization
- you want worked examples of assignments in TensorFlow 2 and NumPy
- you need concise notes to review deep learning concepts for interviews

## When to avoid
- you want a production deep learning framework or library
- you need maintained software with a license for redistribution
- you are looking for original course content rather than student solutions

## Facets
- artifact type: learning-resource
- maturity: stable
- function: deep-learning, machine-learning, developer-tools
- domain: deep-learning, machine-learning, tutorials, education
- platform: python, cross-platform
- tags: coursera, andrew-ng, jupyter-notebooks, assignments, quizzes, cnn, rnn, tensorflow, course-notes

## Member repositories
- amanchadha/coursera-deep-learning-specialization (main) score 71

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:46.341520+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 2026-08-29T18:21:31.528622+00:00, confidence not recorded.
  - readme: https://github.com/amanchadha/coursera-deep-learning-specialization (fetched 2026-08-28T04:08:46.341520+00:00, sha 4309177e5bb2)
- Data as of 2026-08-30T08:39:29.467469+00:00.