# TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials

A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Climate / Energy, Automotives, Retail, Pharma, Medicine, Healthcare, Policy, Ethics and more.

Repository: https://github.com/TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials
Canonical: https://ross.abutalabs.com/products/artificial-intelligence-deep-learning-machine-learning-tutorials
Homepage: https://deepkapha.ai
Language: Python
License: NOASSERTION
License Family: other
Topics: machine-learning, deep-learning, tensorflow, python, pytorch, keras, lua, matplotlib, aws, kaggle, pandas, scikit-learn, torch, artificial-intelligence, neural-network, convolutional-neural-networks, tensorflow-tutorials, python-data, ipython-notebook, capsule-network
Last push: 2025-03-19T09:40:41+00:00

## Health v2 (maintenance only)
Score: 38/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 12, release rhythm 35, longevity 100
- inputs: {"age_days": 3338, "days_push": 532, "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 4002, forks 1625 (observed 2026-08-28T04:08:32.105950+00:00)

## What it is
A curated collection of IPython notebook tutorials covering deep learning, artificial intelligence, and machine learning with frameworks like TensorFlow, PyTorch, and Keras. It also spans industry-specific applications such as healthcare, energy, and retail, and is updated regularly.

## Use cases
- learn deep learning with pytorch notebooks
- find tensorflow tutorials for beginners
- study machine learning examples in python
- explore NLP and computer vision tutorials
- learn keras and scikit-learn through notebooks
- find AI tutorials for healthcare and energy applications

## When to choose
- you want hands-on notebook-based tutorials across many ML/DL frameworks
- you need curated links to probabilistic programming and deep learning examples
- you are exploring industry-specific AI applications

## When to avoid
- you need production-ready code or maintained libraries
- you want a single coherent course rather than a mixed collection
- you require a permissively licensed codebase (license is non-standard)

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, deep-learning, nlp, computer-vision, data-science
- domain: artificial-intelligence, machine-learning, deep-learning, computer-vision, tutorials
- platform: python, cross-platform
- tags: tutorials, ipython-notebooks, tensorflow, pytorch, keras, curated-list, awesome-list, natural-language-processing

## Member repositories
- TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials (main) score 38

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:32.105950+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 2026-08-29T18:24:06.768771+00:00, confidence not recorded.
  - readme: https://github.com/TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials (fetched 2026-08-28T04:08:32.105950+00:00, sha 803b8569c019)
  - homepage: https://deepkapha.ai (fetched 2026-08-29T09:17:12.132296+00:00, sha 44136fa355b3)
- Data as of 2026-08-30T08:39:29.467469+00:00.