[爆卦]algorithm教學是什麼?優點缺點精華區懶人包

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在品牌的社群經營上,Facebook的廣告投放扮演了相當重要的角色,也是許多行銷人致力於專精的工具。如果你還不熟悉這套工具,就跟著我們一起學習FB廣告投放小知識吧!希望這份資料能讓你了解廣告投放的基礎概念和操作方法。這份Facebook Ads的教學手冊。帶領你從目標設定、受眾、版面、預算一步步輕鬆...

algorithm教學 在 工具王 阿璋 Instagram 的最讚貼文

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【#程式教學】程式設計學習資源懶人包 5個學程式的線上平台  學程式最重要的,就是「精神、邏輯思考能力」,許多人短時間速成學好的程式,其實只是一套框架而已,稍微更改一下邏輯就一竅不通了。  學程式的方式有許多種,不外乎是上課、看書、電子書、線上影片等等。 不論是哪一種方式,最重要的就是打好...

  • algorithm教學 在 Taipei Ethereum Meetup Facebook 的最讚貼文

    2021-09-23 21:53:53
    有 6 人按讚

    📜 [專欄新文章] Gas Efficient Card Drawing in Solidity

    ✍️ Ping Chen

    📥 歡迎投稿: https://medium.com/taipei-ethereum-meetup #徵技術分享文 #使用心得 #教學文 #medium

    Assign random numbers as the index of newly minted NFTs

    Scenario

    The fun of generative art NFT projects depends on randomness. The industry standard is “blind box”, where both the images’ serial number and the NFTs’ index are predetermined but will be shifted randomly when the selling period ends. (They call it “reveal”) This approach effectively solves the randomness issue. However, it also requires buyers to wait until the campaign terminates. What if buyers want to know the exact card right away? We’ll need a reliable onchain card drawing solution.

    The creator of Astrogator🐊 isn’t a fan of blind boxes; instead, it thinks unpacking cards right after purchase is more interesting.

    Spec

    When initializing this NFT contract, the creator will determine the total supply of it. And there will be an iterable function that is randomly picking a number from the remaining pool. The number must be in range and must not collide with any existing ones.

    Our top priority is accessibility/gas efficiency. Given that gas cost on Ethereum is damn high nowadays, we need an elegant algorithm to control gas expanse at an acceptable range.

    Achieving robust randomness isn’t the primary goal here. We assume there’s no strong financial incentive to cheat, so the RNG isn’t specified. Implementers can bring their own source of randomness that they think is good enough.

    Implementation

    Overview

    The implementation is pretty short and straightforward. Imagine there’s an array that contains all remaining(unsold) cards. When drawIndex() is called, it generates a (uniform) random seed to draw a card from the array, shortens the array, and returns the selected card.

    Algorithm

    Drawing X cards from a deck with the same X amount of cards is equal to shuffling the deck and dealing them sequentially. It’s not a surprise that our algorithm is similar to random shuffling, and the only difference is turning that classic algo into an interactive version.

    A typical random shuffle looks like this: for an array with N elements, you randomly pick a number i in (0,N), swap array[0] and array[i], then choose another number i in (1,N), swap array[1] and array[i], and so on. Eventually, you’ll get a mathematically random array in O(N) time.

    So, the concept of our random card dealing is the same. When a user mints a new card, the smart contract picks a number in the array as NFT index, then grabs a number from the tail to fill the vacancy, in order to keep the array continuous.

    Tweak

    Furthermore, as long as the space of the NFT index is known, we don’t need to declare/initialize an array(which is super gas-intensive). Instead, assume there’s such an array that the n-th element is n, we don’t actually initialize it (so it is an array only contains “0”) until the rule is broken.

    For the convenience of explanation, let’s call that mapping cache. If cache[i] is empty, it should be interpreted as i instead of 0. On the other hand, when a number is chosen and used, we’ll need to fill it up with another unused number. An intuitive method is to pick a number from the end of the array, since the length of the array is going to decrease by 1.

    By doing so, the gas cost in the worst-case scenario is bound to be constant.

    Performance and limitation

    Comparing with the normal ascending index NFT minting, our random NFT implementation requires two extra SSTORE and one extra SLOAD, which cost 12600 ~ 27600 (5000+20000+2600) excess gas per token minted.

    Theoretically, any instantly generated onchain random number is vulnerable. We can restrict contract interaction to mitigate risk. The mitigation is far from perfect, but it is the tradeoff that we have to accept.

    ping.eth

    Gas Efficient Card Drawing in Solidity was originally published in Taipei Ethereum Meetup on Medium, where people are continuing the conversation by highlighting and responding to this story.

    👏 歡迎轉載分享鼓掌

  • algorithm教學 在 資策會創新學習中心 Facebook 的最佳解答

    2021-02-04 18:20:07
    有 28 人按讚

    讓世界看見臺灣🇹🇼,我們不再是隱形冠軍🏆 #78家智慧學習業者報名參加,🫂近百名業界先進專家同時在線觀看👀【2021 METAEDU MEETUP 第一季產業交流會】圓滿成功🎉

    🏫在教育的議題上,我們從不停下腳步👣,時刻超前佈局,為突如其然的衝擊,迅速提出解方💊,透過教育現場線上線下融合、數位行銷策略、參與國際競賽以及與全球最大科研調查機構Holon IQ合作等方式,繼續大步向前邁進🏃‍♀️🏃

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    精彩回放不錯過 筆記記起來🎬各場次上映時間與特別感謝單位:
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  • algorithm教學 在 資策會創新學習中心 Facebook 的最讚貼文

    2021-01-29 18:00:02
    有 33 人按讚

    #雙倍能量一次到位 由2位博士帶大家一同掌握 #關鍵趨勢 與 #技術核心 🌍#METAEDUMEETUP第一季產業交流會 #0203 14:00線上見👓

    根據UNESCO聯合國教科文組織最新數據顯示,新冠疫情使全球15億學生受到影響、超過8億學生👬面臨教育中斷⏸️ 這只強烈透露一件事☝️全球教育市場的版圖與商業模式👉不得不變!

    〖政策趨勢〗放眼2021國際教育市場
    ❍#資策會數位教育研究所 創新學習中心 莊芳甄主任 帶您從 #國際 #技術 #創新 3個構面,為2021國際教育市場的三角習題🔼提出解方!

    〖技術分享〗教育資料模式識別技術
    ❍#資策會數位教育研究所 教育科技研發中心 游函諺副主任 則要帶領想像未來教育現場的樣態。AI來襲,🖥電腦不再只會選土豆,💫XR、💫xAPI、💫Big Data&Algorithm等,科技融入後教學後,線上整合線下的學習都要更適性,幫助學生學習成效🆙🆙

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    您將收到主辦單位上線連結信件;或請自行關注 經濟部工業局與 資策會創新學習中心的消息
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