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    --- tags: 雜筆 --- # 筆記目錄 * [GIT+VSCODE+MD - 生存工具](https://hackmd.io/T56rQ0CaSj2uuZa8R3gTFw) * [Navigator - JS控制硬體](https://hackmd.io/g5rxNQiNQ120F3E76Xu0Kw) * [Css - 屬性大全](https://hackmd.io/EJ1ec_zBS4CzFgmqBaPR_Q) * [Bootstrap - 物件導向的CSS](https://hackmd.io/fNbgc3_LSG20SlWKjYaejQ) * [Linux - 指令牛刀小試](https://hackmd.io/1AhsGeLNRga6LLTwVQ65ig) * [統計筆記](https://hackmd.io/Zx2flP23T4apmwSYcjBXxQ) * [Py演算法](https://hackmd.io/e_Pkab-vQBaxoRxPgEF7xg) * [FINTECH - 基本、技術、籌碼](https://hackmd.io/FRS5kjdOS5CGcWkyd-ZOeA) * [GIS - 地圖開發](https://hackmd.io/zxw_moTURfG6890L3sDZIw) * [WEBPACK - 打包](https://hackmd.io/tkWGj4THScCtgumJRMkhXA) * [SQL Server - 業界資料庫](https://hackmd.io/xgKzeT3ZQt-a-gTXakndGA) * [Vue - 大補包](https://hackmd.io/0rCTNx-1RrmM2yrQtiscTA) * [TradingVue - 技術分析](https://hackmd.io/dBWiFw-qQIiwGqx5QGrVyQ) * [Yolo - 辨識盤中](https://hackmd.io/JLxKTZ-UTpa0CRL4xoYmsg) * [Firebase - 前端也能操作資料庫](https://hackmd.io/iMCJoAowS0Sbrd9YZQzMTg) ### Excel 必備技巧 |動作|功能| |-|-| |"super"&"cool"|文字串接| |資料>資料剖析|文字切分| |VLOOKUP|配對字串| |檢視>凍結窗格|特定欄位會一直保持在螢幕上| |F4|重複動作、鎖定儲存格| |shift+滑鼠拖移|交換欄位| |shift+enter|換行| |ctrl+shift+方向|快速選取| |ctrl+方向|移到尚有資料的角落| |alt+=|快速選取資料框| |連點兩下儲存格右下角|以下全部套用| |點選左上角全選、連點上方欄位|自動調整欄寬| |資料>移除重複項|清除重複者| |設定格式化的條件|反白特定數值| |格式化為表格|可自由篩選| ### [Pandas V.S. R](https://hackmd.io/s/Syg8AIVyG) ### [資料庫管理](https://hackmd.io/s/ByJTBtFAb) 1. 熟悉CRUD(create, read, update, delete) 2. 批量自動處理(觸發條件自動化) |工具選擇|excel|pandas|SQL| |-|-|-|-| |用途|探索與建檔|統計分析|儲存與分享| |儲存方式|記憶體|記憶體|硬碟| |讀檔方式|特殊編碼(讀取慢但可減少檔案大小)|sql,文字,excel,html|.sql與文字檔| |處理速度|慢|極快|中| |適用大小|百萬筆以下|GB以下|硬碟容量| |資料表合併|資料>新查詢>從表格>合併查詢|pd.merge()|JOIN| ### [爬蟲](https://hackmd.io/s/HyuVGnFJz) 1. 透過網際網路取得資料來源的自動化機器人 2. 內容可以是圖片、網頁中的表格、JSON、XML、HTML文字 ### [資料視覺化](https://hackmd.io/pwbj_ThcQ2GnQ9oNbSzwlQ) > Plotly 一個線上資料分析與視覺化的工具,支援多種語言如python、R、MATLAB、Perl、Julia、Arduino、 REST,在線上模式中可以把圖表同步至雲端、繪製地圖,而在notebook中能夠與圖表互動,例如放大縮小移動、彈跳視窗、一鍵儲存、還可以製作動畫、滑動捲軸。 如果覺得plotly不夠簡單,可以嚐嚐老衲的[**lazyplotly**](https://github.com/billju/lazyplotly) > 套裝軟體 寫程式寫累了嗎?Tableau是個GUI介面的商用資料分析與視覺化的軟體,儘管軟體本身主要是提供企業使用的,他們也推出了免費資料視覺化軟體[Tableau Public](https://public.tableau.com/en-us/s/),或是微軟出品的Power BI,使用群族不在少數 ### [機器學習](https://hackmd.io/bN8NsOKBSF2hpE7Y9monXg) > sklearn 1. 被網友喻為地表最強機器學習套件 2. 從資料集、編碼、降維、分類、回歸、分群一概全包 > mlxtend mlxtend提供sklearn所沒有的關聯分析演算法,其中參數包含 1. 支援度 Support(X->Y) = P(X,Y) (X、Y都發生的機率) 2. 信賴度 Confidence(X->Y) = P(Y|X) = P(X,Y)/P(X) (在X出現的情況下,X、Y都發生的機率) 3. 提升度 Lift(X->Y) = P(Y|X)/P(Y) (大於1代表有強關聯) 4. 槓桿度 Leverage(X->Y) = P(X∩Y) – P(X)P(Y) (等於零代表兩者獨立) 5. 置信度 Conviction(X->Y) = P(X)P(Y')/P(X∩Y') (等於零代表兩者獨立) ![](https://i.imgur.com/mKJSfv3.png) > XGBoost 1. 全名為eXtreme Gradient Boosting 2. 由CART(Classification and Regresion Tree)加上Boosting演算法所組成的,不僅效果好且計算複雜度不高 3. 自2016年釋出以來一直是資料競賽的神器。 4. Windows使用者可從[這裡](https://www.lfd.uci.edu/~gohlke/pythonlibs/)下載wheel檔案,完成後使用指令安裝(注意python版本要一致,此範例為3.7版)`pip install xgboost‑0.90‑cp37‑cp37m‑win_amd64.whl` > LightGBM 1. 於2017/12/2由微軟釋出的Gradient Boosting框架 2. 比XGBoost更輕量、速度更快,是資料科學界的新寵 > tensorflow 1. google在2015年發布了tensorflow,並合併了keras高階API框架,更是大幅降低步入深度學習的門檻 2. 您可能有聽過:人工神經網路、卷積神經網路、遞迴神經網路、自編碼器、生成對抗網路、強化學習,深度學習是這些名詞的總集合 3. 深度學習強項為於高維度的資料,在圖像、語音辨識與生成上有突破性的發展 5. 模型由諸多張量(一維=向量、二維=矩陣、三維以上=張量)組成,適合使用處理器核心極多而單核心不強的顯示卡來運算 6. tensorboard視覺化訓練過程`tensorboard --logdir=ipynb/results/tensorboards` 7. 在終端機輸入`tensorboard --logdir=ipynb/results/tensorboards` 8. 接著會跳出網址`http://localhost:6006/`,以CTRL+左鍵點擊造訪網頁 > pytorch 1. Facebook開源的深度學習框架,tensorboard適合產品,pytorch適合學術研究 2. 僅支援unix作業系統,windows尚未支援 3. 開發與調整參數體驗較好 > keras 1. 以tensorflow、theano或CNTK做為後端的高階程式庫 2. 簡化了張量的處理、模型的設計過程變得更簡單明瞭、用更少的程式碼完成建模,非常適合作為教學使用 > RL 強化學習框架 1. [OpenAI Baselines](https://github.com/openai/baselines) 2. [stable-baselines3](https://github.com/DLR-RM/stable-baselines3) 3. [天授](https://github.com/thu-ml/tianshou) 4. [Ray Project RL-lib](https://github.com/ray-project/ray/tree/master/rllib/) 5. [Deep-Reinforcement-Learning-Algorithms-with-PyTorch](https://github.com/p-christ/Deep-Reinforcement-Learning-Algorithms-with-PyTorch) 6. [rlpyt - Berkekey AI Research](https://github.com/astooke/rlpyt) 7. [garage](https://github.com/rlworkgroup/garage) ### 後端開發 > [Django](https://hackmd.io/s/Sy1Ure_kz) 1. 架構模式上不同於常用的MVC(Model, View, Controller),採用MTV架構(Model–Template–View) 2. 將原本View的部分再細分成Template(存放html)與View(寫http回應),讓前端與後端能夠有更完善的分工 > [Flask](https://hackmd.io/fxoAisnGRCKJLA8msKrTVA) 1. flask是個微框架(microframework),非常輕量、容易上手 2. 相較於django把所有架構都一口氣全包,僅提供最基礎的功能,需要仰賴大量的套件 > [Express]() 1. 基於Node.js > [各框架性能評比](https://www.techempower.com/benchmarks/) ### Awesome系列 1. https://github.com/ybayle/awesome-deep-learning-music 2. https://github.com/keon/awesome-nlp 3. https://github.com/enaqx/awesome-react 4. https://github.com/clvrai/awesome-rl-envs

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