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    # 大三下筆記 [TOC] ## 類神經網路導論 ls -l ls -al // 所有的 AI 很強: 辨識、適應 很弱 記住、推理 AI的演進 * 強調推理的年代(專家系統、小規模的遊戲(下棋)) * 強調訊號處理的年代(類神經網路) 深度學習之父 - Geoffrey Hinton google 翻譯強項:介系詞用法 ![](https://i.imgur.com/5Jrzb3t.png) ![](https://i.imgur.com/EZLALi3.png) "-"代表誤差 ![](https://i.imgur.com/faGd2pU.png) ![](https://i.imgur.com/cqhCt3V.png) why perceptron work 一層神經元 線性組合問題 如果是用 線或面 可以切的 多層神經元 ![](https://i.imgur.com/7hl0kJ1.png) 可以調 epochs batch size 先弄 batch_size 的去做圖片平均 ### ch2 perceptron ## 資料探勘 分類問題 假陽性 假陰性 新冠肺炎 如果檢測標準訂很嚴格 > 數據少 ex. 中國 偏離現實 但數字降低 如果檢測數據訂很鬆 > 數據多 ex. 南韓 確診多 但有可能假陽性會高 資料分析師證照 統計裡面 6最多 考試會有雲端練工坊 核心觀念 > 快 三步驟 搜集資料 分析資料 挖掘資料 觀察 新冠肺炎 巴西很熱 1. 恐慌、囤積 2. 19.5%確診 死亡率1% 數字的前題假設 可能兩者都不對 要考慮醫療是否能負荷 大陸是藥品原料提供國 藥會缺乏 汽車零組件會缺乏 > 分析資料不能只看表面數字 big data ![](https://i.imgur.com/3QENqCM.jpg) IBM 4V + Veracity品質 加上資料的品質 5V + value 價值 [考試題目] 5V hadoop 考試考什麼和hadoop相關 ![](https://i.imgur.com/7wvVvr2.jpg) 資料探勘的演進 統計~1800 影像辨識~1970 有關聯式資料庫 ~1980 KDD從資料發覺 knowledge discoverry datamining 資料探勘流程 要有很多資料 資料倉儲 data warehouse data cleaning ```mermaid graph LR A[Databases] -->B[Data Warehouse] B --> C[Task-relevant Data] C --> D[Pattern EValution] ``` ppt ![](https://i.imgur.com/z4uHglx.png) 群集分析 > 小團體 物以類聚 連結分系 > 購物系統 物品之間關聯 ex 啤酒跟尿布一起買 序列相關 > 先買什麼 再買什麼 鏈結分析 > 網路 交通網路 打電話時間長短 時間序列相關性分析 > 分析過往資料 ex.股票 >相信歷史會重演 ![](https://i.imgur.com/HshJwIg.png) ```mermaid graph LR A[問題] -->B[策略] B --> C[資料探勘 任務] C --> D[技術] ``` ![](https://i.imgur.com/egYEh8O.jpg) data clean data integration !!!非常重要 ![](https://i.imgur.com/AzU7cz4.jpg) 中位數影響 如果 偏左 偏右 或 M型 Q-Q圖 Quantile-Quantile (Q-Q)Plot 期中作業 找資料集 100筆以上 最好不要用已經處理過的 期中作業重點:整理資料 根據一些法則分析結果 domain要自己選 Data cleaning > 年紀不用存,可以從出生年月日求 missing data > 就是不見了 要注意是要怎麼捨棄或補值會不會影響結果 Noisy data 怎麼處理? * bining:把資料使用一組一組 * Regression:直線上的點取代原始值 * clustering:落在群的外面就是雜訊並且刪掉 * 複雜方法或人來處理雜訊 資料要離散化 weka打開 data/iris.arff 資料集 如果讀檔有問題,要改csv成arff 或用這 https://ikuz.eu/csv2arff/ 每個屬性值範圍不同,要做正規化,範圍介在0~1中間 離散化>資料本身有雜訊、不需要看這麼細 discretize 不同的處理,有可能導致準確率變低 unsupervize>discretize unsupervize>IQR(inter quartileRange) IQR:把資料分成1/4 3/4中間 ![](https://i.imgur.com/fA3AZVG.png) instance > removewithvalues 作業要交:有前處理過的資料 正規化 unsupervise > normalize normalize之後要看最後一個屬性有沒有操作到 如果沒有 ignoreClass 設定 true 若資料裡面有中文,要改資料的屬性 作業要解釋資料 最好有上千筆資料 ## 公民與多人社會 平時成績 40 出席及上課表現 兩次作業 期中成績 30 個人報告 期末筆試 30 期末考週紙筆測驗 reinhot2@gmail.com ## 網路通訊程式 ```bash nslookup www.google.com ``` dns : port 53 ## 離散數學 Discrete Mathematics and Its Applications, Global Edition 8 Edition By Kenneth Rosen 期中期末 30% 小考 取兩次 30% 筆記 10% 考試題型 ![](https://i.imgur.com/eYysKu4.png) ## 廣域網路 aux port console console USB-B RIP 簡單的路由表,適用router<10 ![](https://i.imgur.com/gX0USYF.png) ![](https://i.imgur.com/AIHJJIl.png) ![](https://i.imgur.com/7IToXJp.png) ![](https://i.imgur.com/wHrPVc8.png) ![](https://i.imgur.com/rY2IWrK.png) ![](https://i.imgur.com/kMUraD6.png) ![](https://i.imgur.com/zkYERUS.png) ![](https://i.imgur.com/pzM7UsA.jpg) 期中考 ch1-ch6 交換機指派兩個區域網路 練習 router on a stick 其中複習 必考 30 ![](https://i.imgur.com/AgIYPMn.png) CH6 p62 ![](https://i.imgur.com/eDMvkDt.png) ![](https://i.imgur.com/FIu9HKe.png) ![](https://i.imgur.com/7agIbEG.png) ![](https://i.imgur.com/RrDu8ZQ.png) inter vlan routing ch5 switch config 專有名詞 full/half duplux full送收同時進行 half 一次只有送或收 auto mdix ch4 簡答題 交換機運作原理 訓匡資料的轉送:兩種說出不同 1. store and forward 把訓匡資料完整接收到FCS 使用CRC檢查碼檢查是否有資料錯誤 沒有才進行轉送 有的話丟掉它 請重傳 不管PC、router 2. cut through 看第二個欄位 看mac資料要往哪裡丟 ,沒有CRC檢查 ch3 動態路由演算法/協定 RIP 靜態及動態路由比較 優缺點 動態 很多router不會用靜態路由 網路大小無所謂 應映網路大小與流量自動調節 安全度高(路徑會改變) 靜態 複雜 安全度低(路徑可預知)(可加RSA提高安全) 拓補 不可預期 頻寬損耗 ch2 解釋名詞 Q:什麼叫做 default static route? A:route連線只有一條 ip 0.0.0.0 遮罩0.0.0.0 考觀念 ch1 記憶體單元 ram rom nvram 做什麼用途? ram arp表放這 rom 放post程式 硬體檢查 nvram 放備份 flash 放作業系統 5:00考試,考完離開 ACL 防火牆 使用0遮罩 ![](https://i.imgur.com/Hlj7BEb.png) ![](https://i.imgur.com/s0mJVBf.png) ![](https://i.imgur.com/pYHPYCe.png) 第一行不開放某台主機 第二行 開放其他的 ![](https://i.imgur.com/DEgnQI8.png) ㄗ ![](https://i.imgur.com/UMk9kal.png) ![](https://i.imgur.com/mINEwfQ.png)

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