#機器學習
Live, measured metrics for the hashtag #機器學習 from the open social web. Every number carries a named source and the time it was fetched. Nothing is estimated.
Own #機器學習
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.
Day-by-day usage
measured · fosstodon.org (Mastodon public tags API) · fetched 2026-08-21 21:15 UTC12 uses by 7 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 2026-08-21 21:15 UTCLive pulse
measured · fosstodon.org (Mastodon tag timeline) · fetched 2026-08-21 21:15 UTCEverything below is measured over the latest 40 public posts (spanning ~1756 hours).
Posting hours (UTC) — busiest: 20:00
Languages: Chinese (Taiwan) (40)
Avg boosts / post: 0
Top of the latest posts
🌗 揭祕後車廂：Waymo 自動駕駛運算系統深入剖析 ➤ 邁向實體人工智慧的極致效能與安全冗餘 ✤ https://waymo.com/blog/2026/08/look-under-our-trunk/ Waymo 首次公開其自動駕駛大腦「Waymo Driver」的運算硬體細節。為應對複雜路況，Waymo 捨棄通用組件，轉而採取硬體、感測器與演算法協同設計的策略。該系統搭載自主研發的 5nm ASIC 晶片，提供超過 1,000 TOPS 的算力，能直接處理並融合雷達與
🌗 訓練 1.25 億參數模型實現鋼琴自動伴奏——SimEdw 的部落格 ➤ 突破邊緣端運算瓶頸的即時 MIDI 生成技術實踐 ✤ https://simedw.com/2026/08/20/midi-autocomplete/ 作者成功訓練了一個擁有 1.25 億參數的 Transformer 模型，能以每秒約 108 個音符的速度在 iPhone 15 上即時自動生成鋼琴伴奏。本專案的關鍵突破在於捨棄傳統多步驟的 MIDI 事件表徵，改用單一音符的多欄位嵌入表示法，讓大
🌗 Unsloth Dynamic 3.0 GGUF 說明文件 ➤ 打造極致輕量且不犧牲精度的本地 LLM 部署新標準 ✤ https://unsloth.ai/docs/basics/dynamic-3.0-ggufs 本文介紹 Unsloth 推出全新升級的 Dynamic 3.0 GGUF 量化技術。該技術能在保持模型體積不變的前提下，將準確度提升 10% 以上。透過高質量的重要性矩陣（imatrix）校準數據集、智慧動態層選擇，以及純後訓練量化（PTQ）方法，此技術
#機器學習 across platforms
every network with a public tag surfaceFollow #機器學習 straight to each platform’s own tag page. Where a platform publishes open data we measure it above; the rest lock their numbers behind paid APIs, so we link rather than guess.
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/機器學習