電玩遊戲新聞

買了也耍有膽子玩!Steam《絕命精神病院 Outlast》系列史上最低價2折促銷

恐怖遊戲《絕命精神病院 Outlast》系列當前正在Steam上促銷,《絕命精神病院1》2折促銷,不支持中文,《絕命精神病院2》25折促銷,自帶中文。購買合集包補全價格是3折。網友們表示:我是沒錢買嗎?我是沒膽玩。網友:買了不敢玩,只能短片通關。

Outlast-Horror-1280x640

《絕命精神病院2》(英語:Outlast 2,符號化為「OU⸸LASTII」)是由Red Barrels開發兼發行的隱蔽類生存恐怖遊戲。在前作《絕命精神病院》發行並擁有知名度後不久,Red Barrels即宣布《絕命精神病院2》正在製作當中。遊戲的主角為記者夫婦:布雷克(Blake)與琳恩(Lynn),兩人為了調查一宗孕婦離奇被害的案件而前往亞利桑那州的沙漠,但其乘搭的直升機發生意外墜機,夫婦兩人因而失散。布萊克醒來後不見琳恩,為此他必須走進一個恐怖村落中找尋妻子[3]。被童年夢魘所困擾的布雷克正逐步揭開村落背後的秘密。

購買位置:

https://store.steampowered.com/app/238320/Outlast/

https://store.steampowered.com/app/414700/Outlast_2/

 

14,559 thoughts on “買了也耍有膽子玩!Steam《絕命精神病院 Outlast》系列史上最低價2折促銷

  1. I loved as much as you will receive carried out right here.
    The sketch is tasteful, your authored material stylish.

    nonetheless, you command get bought an impatience over that you wish be delivering the following.
    unwell unquestionably come further formerly again as
    exactly the same nearly a lot often inside case you shield this increase.

  2. Dialogue language understanding incorporates two most important elements:
    intent detection and slot filling Young et al.

    3) We introduce a Contrastive Alignment Learning goal to jointly refines the metric areas
    of intent detection and slot filling. Based
    on that, we current two key parts of ConProm: the Prototype Merging mechanism that
    adaptively connects two metric spaces of intent and slot (§3.2) and the Contrastive Alignment Learning
    that jointly refines the metric area connected by Prototype Merging (§3.3).

    After that, we cut up the Snips dataset into 3 components:
    the coaching area with 3 intents, the developing area with 2
    intents and the testing domain with 2 intents. This suggests that, to a point,
    DialoGPT (Zhang et al., 2020) can disambiguate between a primary title and a final
    title when provided concurrently (e.g., ‘my name is Lakesha Mocher’).
    Through the experiment, it is pre-trained on supply
    domains after which instantly applies to target domains with out fantastic-tuning.
    With this natural language reformulation, the slot filling task is being tailored to better leverage the capabilities of the pre-educated DialoGPT model.
    Specifically, we use CrossEntropy (CE) to calculate the loss for intent detection and slot filling.
    In dialogue language understanding task, we joint study
    the intent detection process and slot filling by optimizing both losses at the same
    time.

發佈回覆給「国产线播放免费人成视频播放」的留言 取消回覆

發佈留言必須填寫的電子郵件地址不會公開。 必填欄位標示為 *