#overfitting

Live, measured metrics for the hashtag #overfitting from the open social web. Every number carries a named source and the time it was fetched. Nothing is estimated.

hashtag.org network · sponsored

Own #overfitting

This #name is available to claim. It becomes your portal on the open agent web: this very page, a keyword you rank for by an open public stake, and a verifiable identity for AI agents. Nobody else sells a page like this for every #name.

$10.85/ year · 11-character #name
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card via hashtag.org · tokens via hashtag.space
0
Uses / 7 days
Mastodon
0
Accounts / 7 days
Mastodon
40
Recent posts
Mastodon
~0/hr
Recent pace
Mastodon · last 40
0.1
Avg reactions / post
Mastodon · last 40

Day-by-day usage

measured · fosstodon.org (Mastodon public tags API) · fetched 2026-07-28 04:01 UTC
0
07-22
0
07-23
0
07-24
0
07-25
0
07-26
0
07-27
0
07-28

0 uses by 0 unique accounts across the window. Real per-day counts, not estimates. Newest bar is today so far.

Related hashtags

measured · fosstodon.org (Mastodon public search API) · fetched 2026-07-28 04:01 UTC
#overfittinginai
0 uses · 0 accounts / 7d
#overfittinggeneralization
0 uses · 0 accounts / 7d

Live pulse

measured · fosstodon.org (Mastodon tag timeline) · fetched 2026-07-28 04:01 UTC

Everything below is measured over the latest 40 public posts (spanning ~50307 hours).

Posting hours (UTC) — busiest: 09:00

00:0012:0023:00

Languages: English (36) · Spanish (1) · Russian (1) · German (1) · Basque (1)

Avg boosts / post: 0.3

Top of the latest posts

  • Why Does A.I. Write Like … That? Sam Kriss for the New York Times: """ According to the data, post-ChatGPT papers lean more on words like “underscore,” “highlight” and “showcase” than pre-ChatGPT papers [..] And “delve” [..] shot up by 2,70

    Timo Tijhof@krinkle♥ 2↻ 02025-12-18 16:59 UTCView post →

  • 🔑 3 cuidados esenciales para entrenar redes neuronales: 1️⃣ Minimiza el error cuadrático medio – Ajusta los pesos de forma iterativa para acercarte a la realidad. 2️⃣ Normaliza y escala los datos – Evita que variables con rangos grandes di

    Jefferson Samillan@[email protected]♥ 0↻ 02026-06-22 21:37 UTCView post →

  • Sudo su (@sudoingX) 훈련 예시 수를 늘리는 데이터 스케일링이 단순히 더 큰 네트워크를 만드는 것보다 일반화 성능에 유리하다는 점을 차트로 설명합니다. 학습 데이터에는 96%까지 올라가지만 보지 못한 데이터에서는 성능이 갈리는 모습을 보여, 소규모 데이터에서의 과적합과 데이터 확장의 중요성을 강조합니다. https://x.com/sudoingX/status/2063337880613966012 #datascalin

    ainews@[email protected]♥ 0↻ 02026-06-07 00:49 UTCView post →

Every number above is measured from a named public API at the shown fetch time. Nothing is estimated or extrapolated. Platforms that lock their data behind paid APIs are not shown. Agents: the same numbers, as JSON, at /api/hashtags/overfitting