labmlai/annotated_deep_learning_paper_implementations — GitHub trend

FINDING #7486 · UNIT ID 290091948

labmlai/annotated_deep_learning_paper_implementations

🧑‍🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

[ PYTHON ][ ORG ][ VERIFIED ][ GITHUB ↗ ]

SURPRISE SCORE

0.00

Score Breakdown

SURPRISE0.00018

ENGAGEMENT0.42

FRESHNESS1.00

SCORE = SURPRISE × ENGAGEMENT^0.7 × FRESHNESS × VISIBILITY × CONFIDENCE

SURPRISE = WINDOW STARS / DAYS / (AUDIENCE + FLOOR)

0% OF STARS IN ARCHIVE

Growth Telemetry

VELOCITY /D

7.33

ACCEL

-0.50

RETENTION

66.7%

PEAK 2026-09-11 · FORK-RETENTION 0.0% · 22 STARS / WINDOW

Author Audience

AUDIENCE

40,753

FOLLOWERS

2,841

OWNER ★

71,056

Engagement Signals

FORKS

6,759

ISSUE AUTH

PR AUTH

UNIQUE STARGAZERS 22 / 22 (DIVERSITY 1.00)

Why This Is A Finding

labmlai/annotated_deep_learning_paper_implementations собрал 22 звёзд за окно, тогда как у автора всего 2,841 подписчиков — эффективная аудитория ≈ 40,753. Это даёт surprise-индекс 0.00018 (звёзды относительно охвата автора, а не в абсолюте). Удержание форков 0.0% и 0 внешних контрибьюторов отделяют реальный инструмент от разовой вспышки. Акселерация отрицательная — внимание остывает после пика.

Related Findings

RANKS ABOVE 15% OF 8833 FINDINGS

#02jordan-gibbs/hyperresearch[ PYTHON ]1.33 #03ayghri/i-have-adhd[ PYTHON ]1.18 #05siknet/FreePEP[ PYTHON ]1.11 #14aldegad/sprite-gen[ PYTHON ]0.65 #19HarnessRouter/harnessrouter[ PYTHON ]0.54

METRICS IN CONTEXT

MEDIAN ACROSS ALL 8833 FINDINGS · Δ vs MEDIAN · PERCENTILE = SHARE RANKED BELOW

METRICVALUEMEDIANΔ MEDPERCENTILE

SCORE0.000.00-0.00ABOVE 15%

VELOCITY7.335.67+1.67ABOVE 59%

RETENTION66.7%45.9%+20.8 PPABOVE 73%

FORKS6,759344+6,415ABOVE 95%

SURPRISE0.000.00-0.00ABOVE 9%