# hesamsheikh/ml-retreat

Machine Learning Journal for Intermediate to Advanced Topics.

Repository: https://github.com/hesamsheikh/ml-retreat
Canonical: https://ross.abutalabs.com/products/ml-retreat
Homepage: https://x.com/Hesamation
Language: Jupyter Notebook
License Family: other
Topics: data-science, documentation, large-language-models, learning-resources, llm, machine-learning, machine-learning-algorithms, study-notes
Last push: 2025-09-08T14:23:19+00:00

## Health v2 (maintenance only)
Score: 41/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 41, release rhythm 35, longevity 51
- inputs: {"age_days": 714, "days_push": 359, "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 2371, forks 252 (observed 2026-08-28T04:06:41.747060+00:00)

## What it is
A personal learning journal of intermediate-to-advanced machine learning topics, maintained as Jupyter notebooks and PDF study notes. It covers LLMs from scratch, hallucination, attention mechanisms, graph neural networks, and mechanistic interpretability with curated reading lists.

## Use cases
- study advanced machine learning topics with structured notes
- learn how large language models work from scratch
- understand LLM hallucination and attention mechanisms
- get an introduction to graph neural networks
- follow a curated list of must-read ML research papers
- find supplementary resources for deep learning study
- review notes on AlphaFold 3 and NLP fundamentals

## When to choose
- you want well-organized study notes on intermediate/advanced ML and LLM topics
- you prefer learning from curated papers, videos, and annotated notebooks
- you want a structured day-by-day curriculum for self-study

## When to avoid
- you need production-ready ML code or libraries
- you want a complete formal course with exercises and grading
- you need beginner-level introductory ML tutorials

## Facets
- artifact type: learning-resource
- maturity: active
- function: documentation, machine-learning, nlp
- domain: machine-learning, deep-learning, large-language-models, tutorials, data-science
- platform: python
- tags: study-notes, llm, transformers, graph-neural-networks, mechanistic-interpretability, jupyter-notebooks, learning-journal

## Member repositories
- hesamsheikh/ml-retreat (main) score 41

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:41.747060+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 2026-08-30T02:35:22.383200+00:00, confidence not recorded.
  - readme: https://github.com/hesamsheikh/ml-retreat (fetched 2026-08-28T04:06:41.747060+00:00, sha d9dfe25a8452)
- Data as of 2026-08-30T08:39:29.467469+00:00.