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allowfullscreen=\"allowfullscreen\"></iframe>\r\n<br>\r\n<h4># 0805 ChatGPT  </h4>\r\n<iframe src=\"https://player.vimeo.com/video/852895457\" width=\"640\" height=\"360\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"></iframe>\r\n<br>\r\n<h4># 0805 手把手預備 </h4>\r\n<iframe src=\"https://player.vimeo.com/video/852910511\" width=\"640\" height=\"360\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"></iframe>\r\n<br>\r\n\r\n<script src=\"https://player.vimeo.com/api/player.js\"></script>','北部智慧醫療專班第六期#0805','','publish','open','open','','calendar-ah121-3','','','2023-08-09 15:14:31','2023-08-09 07:14:31','',0,'https://mgr.aiacademy.tw/?p=3715',0,'post','',0),(3716,113,'2023-08-09 15:12:55','2023-08-09 07:12:55','<h3>北部智慧醫療專班第六期 #第三週影片</h3>\r\n\r\n<h4># 0805 自然語言處理技術在智慧醫療的應用 I </h4>\r\n<iframe src=\"https://player.vimeo.com/video/852863374\" width=\"640\" height=\"360\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"></iframe>\r\n<br>\r\n<h4># 0805 自然語言處理技術在智慧醫療的應用 II 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width=\"640\" height=\"360\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"></iframe>\r\n<br>\r\n<h4># 0805 自然語言處理技術在智慧醫療的應用 II </h4>\r\n<iframe src=\"https://player.vimeo.com/video/852874706\" width=\"640\" height=\"360\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"></iframe>\r\n<br>\r\n<h4># 0805 ChatGPT  </h4>\r\n<iframe src=\"https://player.vimeo.com/video/852895457\" width=\"640\" height=\"360\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"></iframe>\r\n<br>\r\n<h4># 0805 手把手預備 </h4>\r\n<iframe src=\"https://player.vimeo.com/video/852910511\" width=\"640\" height=\"360\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"></iframe>\r\n<br>\r\n\r\n<script src=\"https://player.vimeo.com/api/player.js\"></script>','北部智慧醫療專班第六期#0805','','inherit','closed','closed','','3715-revision-v1','','','2023-08-09 15:14:31','2023-08-09 07:14:31','',3715,'https://mgr.aiacademy.tw/3715-revision-v1/',0,'revision','',0),(3720,124,'2023-08-11 13:31:00','2023-08-11 05:31:00','<p><strong>績優智慧製造AI產業化創新競賽範本</strong><p>\r\n<p>為了使各組在AI產業化創新競賽報告製作上能更有雛型與想像，特請學長姊提供績優範本給大家參考。\r\n<p>智慧製造績優範本：<a href=\"https://drive.google.com/file/d/1fwwrHW7NmFEvlaYkbJE9Q7toBM9UtT2B/view?usp=sharing\">https://drive.google.com/file/d/1fwwrHW7NmFEvlaYkbJE9Q7toBM9UtT2B/view?usp=sharing</a>\r\n<p>智慧製造績優範本：<a href=\"https://drive.google.com/file/d/1Kiw55pgbndBePJoJZPEXx45xGa_QADEX/view?usp=sharing\">https://drive.google.com/file/d/1Kiw55pgbndBePJoJZPEXx45xGa_QADEX/view?usp=sharing</a>\r\n<p>智慧製造績優範本：<a href=\"https://drive.google.com/file/d/1nM0ythbyPtHhg9uzmN4QmIBdnOgnzgV1/view?usp=sharing\">https://drive.google.com/file/d/1nM0ythbyPtHhg9uzmN4QmIBdnOgnzgV1/view?usp=sharing</a>\r\n','智慧製造AI產業化創新競賽範本','','inherit','closed','closed','','3082-autosave-v1','','','2023-08-11 13:31:00','2023-08-11 05:31:00','',3082,'https://mgr.aiacademy.tw/3082-autosave-v1/',0,'revision','',0),(3721,124,'2023-08-11 13:31:22','2023-08-11 05:31:22','<p><strong>績優智慧製造AI產業化創新競賽範本</strong><p>\r\n<p>為了使各組在AI產業化創新競賽報告製作上能更有雛型與想像，特請學長姊提供績優範本給大家參考。\r\n<p>智慧製造績優範本：<a href=\"https://drive.google.com/file/d/1fwwrHW7NmFEvlaYkbJE9Q7toBM9UtT2B/view?usp=sharing\">https://drive.google.com/file/d/1fwwrHW7NmFEvlaYkbJE9Q7toBM9UtT2B/view?usp=sharing</a>\r\n<p>智慧製造績優範本：<a href=\"https://drive.google.com/file/d/1Kiw55pgbndBePJoJZPEXx45xGa_QADEX/view?usp=sharing\">https://drive.google.com/file/d/1Kiw55pgbndBePJoJZPEXx45xGa_QADEX/view?usp=sharing</a>\r\n<p>智慧製造績優範本：<a href=\"https://drive.google.com/file/d/1nM0ythbyPtHhg9uzmN4QmIBdnOgnzgV1/view?usp=sharing\">https://drive.google.com/file/d/1nM0ythbyPtHhg9uzmN4QmIBdnOgnzgV1/view?usp=sharing</a>\r\n','智慧製造AI產業化創新競賽範本','','inherit','closed','closed','','3082-revision-v1','','','2023-08-11 13:31:22','2023-08-11 05:31:22','',3082,'https://mgr.aiacademy.tw/3082-revision-v1/',0,'revision','',0),(3725,124,'2023-08-17 09:33:11','2023-08-17 01:33:11','<h3>AM127-台北總校第十六期_經理人研修班 #第5週viedo</h3>\r\n\r\n<h4># 0812 手把手實作 & 當數據遇見問題的實務經驗談  </h4>\r\n<iframe src=\"https://player.vimeo.com/video/855212200\" width=\"960\" height=\"540\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"></iframe>\r\n<br>\r\n\r\n<script src=\"https://player.vimeo.com/api/player.js\"></script>','AM127台北16期_經理人研修班 #0812','','publish','open','open','','am127-viedo_w5','','','2023-11-15 10:46:49','2023-11-15 02:46:49','',0,'https://mgr.aiacademy.tw/?p=3725',0,'post','',0),(3726,124,'2023-08-17 09:33:02','2023-08-17 01:33:02','<h3>AM127-台北總校第十六期_經理人研修班 #第4週viedo</h3>\r\n\r\n<h4># 0812 手把手實作 & 當數據遇見問題的實務經驗談  </h4>\r\n<iframe src=\"https://player.vimeo.com/video/855212200\" width=\"960\" height=\"540\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"></iframe>\r\n<br>\r\n\r\n<script src=\"https://player.vimeo.com/api/player.js\"></script>','AM127台北16期_經理人研修班 #0812','','inherit','closed','closed','','3725-revision-v1','','','2023-08-17 09:33:02','2023-08-17 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src=\"https://player.vimeo.com/video/856670671\" width=\"640\" height=\"360\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"></iframe>\r\n<br>\r\n<h4># 0820 智慧醫療監管與責任法制 </h4>\r\n<iframe src=\"https://player.vimeo.com/video/856704614\" width=\"640\" height=\"360\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"></iframe>\r\n<br>\r\n<script src=\"https://player.vimeo.com/api/player.js\"></script>','北部智慧醫療專班第六期#0820','','publish','open','open','','calendar-ah121-4','','','2023-08-23 09:27:06','2023-08-23 01:27:06','',0,'https://mgr.aiacademy.tw/?p=3729',0,'post','',0),(3730,113,'2023-08-22 17:17:31','2023-08-22 09:17:31','<h3>北部智慧醫療專班第六期 #第四週影片</h3>\r\n\r\n<h4># 0820 醫療實作案例分享 I </h4>\r\n<iframe src=\"https://player.vimeo.com/video/856610997\" width=\"640\" height=\"360\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"></iframe>\r\n<br>\r\n<h4># 0820 醫療實作案例分享 II </h4>\r\n<iframe src=\"https://player.vimeo.com/video/856623616\" width=\"640\" height=\"360\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"></iframe>\r\n<br>\r\n<h4># 0820 Business Models for Medical AI </h4>\r\n<iframe src=\"https://player.vimeo.com/video/856643697\" width=\"640\" height=\"360\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"></iframe>\r\n<br>\r\n<h4># 0820 智慧醫療與健康資料治理 </h4>\r\n<iframe src=\"https://player.vimeo.com/video/856670671\" width=\"640\" height=\"360\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"></iframe>\r\n<br>\r\n<h4># 0820 智慧醫療監管與責任法制 </h4>\r\n<iframe src=\"https://player.vimeo.com/video/856698984\" width=\"640\" height=\"360\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"></iframe>\r\n<br>\r\n<script src=\"https://player.vimeo.com/api/player.js\"></script>','北部智慧醫療專班第六期#0820','','inherit','closed','closed','','3729-revision-v1','','','2023-08-22 17:17:31','2023-08-22 09:17:31','',3729,'https://mgr.aiacademy.tw/3729-revision-v1/',0,'revision','',0),(3732,113,'2023-08-23 09:25:51','2023-08-23 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height=\"540\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"></iframe>\r\n<br>\r\n<script src=\"https://player.vimeo.com/api/player.js\"></script>','AM129台北18期_經理人研修班 #1111','','inherit','closed','closed','','3786-revision-v1','','','2023-11-14 10:25:40','2023-11-14 02:25:40','',3786,'https://mgr.aiacademy.tw/3786-revision-v1/',0,'revision','',0),(3789,113,'2023-11-14 11:35:36','2023-11-14 03:35:36','<h3>AM129-台北總校第十八期_經理人研修班 #第1週viedo</h3>\r\n\r\n<h4># 1111 開學典禮上午場 </h4>\r\n<iframe src=\"https://player.vimeo.com/video/884212153\" width=\"960\" height=\"540\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"></iframe>\r\n<br>\r\n\r\n<h4># 1111 開學典禮下午場 </h4>\r\n<iframe src=\"https://player.vimeo.com/video/884214057\" width=\"960\" height=\"540\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"></iframe>\r\n<br>\r\n<script src=\"https://player.vimeo.com/api/player.js\"></script>','AM129台北18期_經理人研修班 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height=\"540\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"></iframe>\r\n<br>\r\n<h4># 0106 AIGC圖像生成 </h4>\r\n<iframe src=\"https://player.vimeo.com/video/901004515\" width=\"960\" height=\"540\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"></iframe>\r\n<br>\r\n<script src=\"https://player.vimeo.com/api/player.js\"></script>\r\n\r\n','AM129 台北18期_經理人研修班 #0106','','publish','open','open','','mgr-video-129_w8','','','2024-01-09 10:51:25','2024-01-09 02:51:25','',0,'https://mgr.aiacademy.tw/?p=3846',0,'post','',0),(3847,113,'2024-01-09 10:51:25','2024-01-09 02:51:25','<h3>AM129-台北總校第十八期_經理人研修班 #第8週video</h3>\r\n\r\n<h4># 0106 幻想與創意-AI生成如何加速視覺溝通 </h4>\r\n<iframe src=\"https://player.vimeo.com/video/900316148\" width=\"960\" height=\"540\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"></iframe>\r\n<br>\r\n<h4># 0106 AIGC圖像生成 </h4>\r\n<iframe src=\"https://player.vimeo.com/video/901004515\" width=\"960\" height=\"540\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"></iframe>\r\n<br>\r\n<script src=\"https://player.vimeo.com/api/player.js\"></script>\r\n\r\n','AM129 台北18期_經理人研修班 #0106','','inherit','closed','closed','','3846-revision-v1','','','2024-01-09 10:51:25','2024-01-09 02:51:25','',3846,'https://mgr.aiacademy.tw/3846-revision-v1/',0,'revision','',0),(3849,113,'2024-01-23 10:59:54','2024-01-23 02:59:54','<h3>AM129-台北總校第十八期_經理人研修班 #第9週video</h3>\r\n\r\n<h4># 0120 社群媒體 </h4>\r\n<iframe src=\"https://player.vimeo.com/video/905425796\" width=\"960\" height=\"540\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\">\r\n<br>\r\n<script src=\"https://player.vimeo.com/api/player.js\"></script>\r\n\r\n','AM129 台北18期_經理人研修班 #0120','','publish','open','open','','mgr-video-129_w9','','','2024-01-23 10:59:54','2024-01-23 02:59:54','',0,'https://mgr.aiacademy.tw/?p=3849',0,'post','',0),(3850,113,'2024-01-23 10:59:54','2024-01-23 02:59:54','<h3>AM129-台北總校第十八期_經理人研修班 #第9週video</h3>\r\n\r\n<h4># 0120 社群媒體 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src=\"https://player.vimeo.com/api/player.js\"></script>','AM131台北19期_經理人研修班 #0316','','publish','open','open','','mgr-am131-viedo_w1','','','2024-03-19 15:06:56','2024-03-19 07:06:56','',0,'https://mgr.aiacademy.tw/?p=3910',0,'post','',0),(3912,124,'2024-03-19 14:50:32','2024-03-19 06:50:32','<h3>AM131-台北總校第十九期_經理人研修班 #第1週viedo</h3>\r\n\r\n<h4># 0316 開學典禮 & 主題演講 </h4>\r\n<iframe src=\"https://player.vimeo.com/video/924892640\" width=\"960\" height=\"540\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"></iframe>\r\n<br>\r\n\r\n<script src=\"https://player.vimeo.com/api/player.js\"></script>','AM131台北19期_經理人研修班 #0316','','inherit','closed','closed','','3910-revision-v1','','','2024-03-19 14:50:32','2024-03-19 06:50:32','',3910,'https://mgr.aiacademy.tw/3910-revision-v1/',0,'revision','',0),(3913,124,'2024-03-19 14:56:18','2024-03-19 06:56:18','<h3>AM131-台北總校第十九期_經理人研修班 #第1週viedo</h3>\r\n\r\n<h4># 0316 開學典禮 & 主題演講 </h4>\r\n<iframe src=\"https://player.vimeo.com/video/924892640\" width=\"960\" height=\"540\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"></iframe>\r\n<br>\r\n\r\n<script src=\"https://player.vimeo.com/api/player.js\"></script>','AM131台北19期_經理人研修班 #0316','','inherit','closed','closed','','3910-revision-v1','','','2024-03-19 14:56:18','2024-03-19 06:56:18','',3910,'https://mgr.aiacademy.tw/3910-revision-v1/',0,'revision','',0),(3914,124,'2024-03-27 09:02:22','2024-03-27 01:02:22','<h3>AM131-台北總校第十九期_經理人研修班 #第2週viedo</h3>\r\n\r\n<h4># 0323 機器學習的原理與實務 </h4>\r\n<iframe src=\"https://player.vimeo.com/video/927477414\" width=\"960\" height=\"540\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"></iframe>\r\n<br>\r\n\r\n<script src=\"https://player.vimeo.com/api/player.js\"></script>','AM131台北19期_經理人研修班 #0323','','publish','open','open','','mgr-am131-viedo_w2','','','2024-03-27 09:02:22','2024-03-27 01:02:22','',0,'https://mgr.aiacademy.tw/?p=3914',0,'post','',0),(3915,124,'2024-03-27 09:02:17','2024-03-27 01:02:17','<h3>AM131-台北總校第十九期_經理人研修班 #第2週viedo</h3>\r\n\r\n<h4># 0323 機器學習的原理與實務 </h4>\r\n<iframe src=\"https://player.vimeo.com/video/927477414\" width=\"960\" height=\"540\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"></iframe>\r\n<br>\r\n\r\n<script src=\"https://player.vimeo.com/api/player.js\"></script>','AM131台北19期_經理人研修班 #0323','','inherit','closed','closed','','3914-revision-v1','','','2024-03-27 09:02:17','2024-03-27 01:02:17','',3914,'https://mgr.aiacademy.tw/3914-revision-v1/',0,'revision','',0),(3918,124,'2024-04-02 14:09:01','2024-04-02 06:09:01','<h3>AM131-台北總校第十九期_經理人研修班 #第3週viedo</h3>\r\n\r\n<h4># 0330 資料科學與大數據分析 </h4>\r\n<iframe src=\"https://player.vimeo.com/video/929743778\" width=\"960\" height=\"540\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"></iframe>\r\n<br>\r\n\r\n<script src=\"https://player.vimeo.com/api/player.js\"></script>\r\n<br><br>\r\n<h3>吳漢銘老師 Slido_Q&A 答覆如下：</h3><br>\r\n\r\n(1)<br>\r\nAnonymous: 關於機器學習+深度學習與統計學相關的參考書，因為時間無法全數閱讀。若請老師只推1~2本的話，請問老師會推薦那本比較適合我們想更瞭解的入門學習者參考?<br>\r\n<strong>hmwu: </strong>推薦以下兩本中文書:<br>\r\n(1) 機器學習的統計基礎：深度學習背後的核心技術，作者： 黃志勝，出版社：旗標，出版日期：2021/11/15。<br>\r\n(2) 深度學習的數學地圖：用 Python 實作神經網路的數學模型，作者： Masanori Akaishi ，譯者： 章奇煒, 王心薇，出版社：旗標，出版日期：2020/05/28。<br>\r\n<br>\r\n\r\n(2)<br>\r\nAnonymous: 是否所有數據都要設法標準化才能開始計算與預估呢？<br>\r\n<strong>hmwu: </strong>基本上，不是所有的資料分析或機器學習方法都需要先做標準化(standardization)(或常規化normalization)，但在很多情況下，這一資料前置處理是建議要做的。<br>\r\n例如: (a) KNN, SVM, K-means這些方法對於資料的尺度(scale)非常敏感，未標準化的資料可能導致算法表現不佳。(b) 線性回歸、邏輯回歸、神經網絡等這些方法在找最佳解時，標準化後的資料可幫助加快梯度下降法的收斂速度，並提高訓練穩定性和。(c) 決策樹、隨機森林、梯度提升樹(GBM)等方法較不重視變數的具體數值，而是關注變數的排序和結構性分割點，此時資料標準化通常不是太必需的。<br>\r\n<br>\r\n\r\n(3)<br>\r\n佘祥麟: 上午第二節的17頁，政大的同記學能力: 16000總學生數，100人抽樣出來，那剩下的15900人如何歸類?<br>\r\n<strong>hmwu: </strong>不太懂這個問題。但老師試著回答。若問題是想了解「政大學生的統計學能力」，那母體就是所有政大學生，總數是16000。因某些原因(成本、時間、技術)問題，無法全部一起同時舉辦一次會考，故會隨機採抽樣的方式，選取100人來舉辦考試，由這些學生(樣本)的平均成績來推論母體的平均成績。在進行隨機抽樣時，所選取的100人樣本用於估計或推論整個政治大學16000名學生群體的統計學能力。這100人樣本被視為整個群體的代表。在統計學中，我們假定這100人樣本是隨機選擇的，因此能夠在一定的信心水準下反映出全體學生的統計學能力，這100人樣本提供了對全體學生統計學能力的一個估計，也就是通過對抽樣出的100人進行分析，我們試圖推斷整個母體(包括這15900人)的統計特性。未被選中的15900人，作為群體的其餘部分，在分析中不直接被觀察或評估。理想情況下，如果抽樣過程是公正且隨機的，樣本的統計特性應該接近於整個群體的特性。因此，雖然這15900人沒有直接參與分析，他們仍然是研究結果推論和結論的一部分。<br>\r\n<br>\r\n\r\n(4)<br>\r\nAnonymous: 請問p. 16的「激活函數」是什麼意思？<br>\r\n<strong>hmwu: </strong>深度學習中，激活函數(activation function)(或啟動函數)是一種數學函數，用於決定神經元是否應該被激活，它決定了神經元的輸出是否應該計入到下一層作為輸入。另外，激活函數的主要作用是截取到資料中的非線性關係到深度學習模型中，使得神經網絡能夠學習和執行更複雜的任務，如圖像識別、語音識別等。若沒有使用激活函數，無論神經網絡的層數有多少，最後的輸出都只是輸入的線性組合，這就會限制了神經網絡模型的預測能力。<br>\r\n<br>\r\n\r\n(5)<br>\r\nAnonymous: P1-8 standard error與standard deviation的差別在哪裡?<br>\r\n<strong>hmwu:</strong> 標準差(standard deviation)用來衡量一組資料點和它的平均數之間的離散程度，是描述單一資料集的分佈特性，亦即用於描述資料集內部的變異性。而標準誤(Standard error)是用來衡量一個樣本統計量(例如: 樣本平均數)對於母體參數(例如: 母體平均數)估計的精確度。以樣本平均數為例子，標準誤是衡量樣本平均數的可靠程度，而不是原始資料的離散程度。所以，標準誤是樣本平均值的標準差(這裡要想到老師講過的抽樣分佈概念)，它隨樣本大小的增加而減小。<br>\r\n<br>\r\n\r\n(6)<br>\r\n李忠穎: 感謝老師深入淺出的介紹！❤️<br>\r\n<strong>hmwu: </strong>謝謝哦~~請記得給5星好評~ 感恩~<br>\r\n<br>\r\n\r\n(7)<br>\r\nAnonymous: 想請老師能分享對 Julia 語言的想法？<br>\r\n<strong>hmwu: </strong>老師對Julia不清楚，但知道它是這時代新開發出來的程式語言，針對科學計算、大數據分析和機器學習領域具有高性能的表現。若是從事這方面的工作，可嚐試去學習，甚至專精，提昇自己較獨特的競爭力。因Julia 社群規模相較R/Python小，發展史較短，未來是否會更趨於小眾，還是發展更廣泛，就難掌握。不同的程式語言，有它們各自的特點及強項，每個人時間都有限，無法全部學會，但至少要精通一項程式語言。<br>\r\n<br>\r\n\r\n(8)<br>\r\nAnonymous: P18, 關鍵字誰訂?<br>\r\n<strong>hmwu: </strong>此問題應該是在問講義S4的第18頁「Text Analysis of Trump’s Tweets with R Code」? 完整問題，應該是「文字探勘(text mining)中，如何定義關鍵字?」。文字探勘(Text Mining)領域中，有所謂詞庫(Corpus)、詞典(Lexicon)、停用詞列表(Stop Words List)、情感詞典(Sentiment Lexicon)、同義詞詞典和反義詞詞典(Thesaurus)，和本體論(Ontology)等等，裡面會預先定義一些詞來表達語義。當然，還有其它方式來定義關鍵字，例如: 做詞頻分析選擇詞頻高的詞為關鍵字、計算TF-IDF選擇區分高的詞為關鍵字、利用NLP技術識別出文本中的命名實體為關鍵字、進行詞彙網絡關係分析找出密切相關的關鍵字、或分析者依照領域專家建議的自定詞也可以為關鍵字。<br><br>','AM131台北19期_經理人研修班 #0330','','publish','open','open','','mgr-am131-viedo_w3','','','2025-02-04 13:47:55','2025-02-04 05:47:55','',0,'https://mgr.aiacademy.tw/?p=3918',0,'post','',0),(3919,124,'2024-04-02 14:05:00','2024-04-02 06:05:00','<h3>AM131-台北總校第十九期_經理人研修班 #第3週viedo</h3>\r\n\r\n<h4># 0330 資料科學與大數據分析 </h4>\r\n<iframe src=\"https://player.vimeo.com/video/929743778\" width=\"960\" height=\"540\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"></iframe>\r\n<br>\r\n\r\n<script src=\"https://player.vimeo.com/api/player.js\"></script>\r\n\r\n吳漢銘老師 Slido_Q&A 答覆如下：\r\n\r\n(1)\r\nAnonymous: 關於機器學習+深度學習與統計學相關的參考書，因為時間無法全數閱讀。若請老師只推1~2本的話，請問老師會推薦那本比較適合我們想更瞭解的入門學習者參考?\r\nhmwu: 推薦以下兩本中文書:\r\n(1) 機器學習的統計基礎：深度學習背後的核心技術，作者： 黃志勝，出版社：旗標，出\r\n版日期：2021/11/15。\r\n(2) 深度學習的數學地圖：用 Python 實作神經網路的數學模型，作者： Masanori Akaishi ，譯者： 章奇煒, 王心薇，出版社：旗標，出版日期：2020/05/28。\r\n\r\n\r\n(2)\r\nAnonymous: 是否所有數據都要設法標準化才能開始計算與預估呢？\r\nhmwu: 基本上，不是所有的資料分析或機器學習方法都需要先做標準化(standardization)(或常規化normalization)，但在很多情況下，這一資料前置處理是建議要做的。\r\n例如: (a) KNN, SVM, K-means這些方法對於資料的尺度(scale)非常敏感，未標準化的資料可能導致算法表現不佳。(b) 線性回歸、邏輯回歸、神經網絡等這些方法在找最佳解時，標準化後的資料可幫助加快梯度下降法的收斂速度，並提高訓練穩定性和。(c) 決策樹、隨機森林、梯度提升樹(GBM)等方法較不重視變數的具體數值，而是關注變數的排序和結構性分割點，此時資料標準化通常不是太必需的。\r\n\r\n\r\n(3)\r\n佘祥麟: 上午第二節的17頁，政大的同記學能力: 16000總學生數，100人抽樣出來，那剩下的15900人如何歸類?\r\nhmwu: 不太懂這個問題。但老師試著回答。若問題是想了解「政大學生的統計學能力」，那母體就是所有政大學生，總數是16000。因某些原因(成本、時間、技術)問題，無法全部一起同時舉辦一次會考，故會隨機採抽樣的方式，選取100人來舉辦考試，由這些學生(樣本)的平均成績來推論母體的平均成績。在進行隨機抽樣時，所選取的100人樣本用於估計或推論整個政治大學16000名學生群體的統計學能力。這100人樣本被視為整個群體的代表。在統計學中，我們假定這100人樣本是隨機選擇的，因此能夠在一定的信心水準下反映出全體學生的統計學能力，這100人樣本提供了對全體學生統計學能力的一個估計，也就是通過對抽樣出的100人進行分析，我們試圖推斷整個母體(包括這15900人)的統計特性。未被選中的15900人，作為群體的其餘部分，在分析中不直接被觀察或評估。理想情況下，如果抽樣過程是公正且隨機的，樣本的統計特性應該接近於整個群體的特性。因此，雖然這15900人沒有直接參與分析，他們仍然是研究結果推論和結論的一部分。\r\n\r\n\r\n(4)\r\nAnonymous: 請問p. 16的「激活函數」是什麼意思？\r\nhmwu: 深度學習中，激活函數(activation function)(或啟動函數)是一種數學函數，用於決定神經元是否應該被激活，它決定了神經元的輸出是否應該計入到下一層作為輸入。另外，激活函數的主要作用是截取到資料中的非線性關係到深度學習模型中，使得神經網絡能夠學習和執行更複雜的任務，如圖像識別、語音識別等。若沒有使用激活函數，無論神經網絡的層數有多少，最後的輸出都只是輸入的線性組合，這就會限制了神經網絡模型的預測能力。\r\n\r\n\r\n(5)\r\nAnonymous: P1-8 standard error與standard deviation的差別在哪裡?\r\nhmwu: 標準差(standard deviation)用來衡量一組資料點和它的平均數之間的離散程度，是描述單一資料集的分佈特性，亦即用於描述資料集內部的變異性。而標準誤(Standard error)是用來衡量一個樣本統計量(例如: 樣本平均數)對於母體參數(例如: 母體平均數)估計的精確度。以樣本平均數為例子，標準誤是衡量樣本平均數的可靠程度，而不是原始資料的離散程度。所以，標準誤是樣本平均值的標準差(這裡要想到老師講過的抽樣分佈概念)，它隨樣本大小的增加而減小。\r\n\r\n\r\n(6)\r\n李忠穎: 感謝老師深入淺出的介紹！❤️\r\nhmwu: 謝謝哦~~請記得給5星好評~ 感恩~\r\n\r\n\r\n(7)\r\nAnonymous: 想請老師能分享對 Julia 語言的想法？\r\nhmwu: 老師對Julia不清楚，但知道它是這時代新開發出來的程式語言，針對科學計算、大數據分析和機器學習領域具有高性能的表現。若是從事這方面的工作，可嚐試去學習，甚至專精，提昇自己較獨特的競爭力。因Julia 社群規模相較R/Python小，發展史較短，未來是否會更趨於小眾，還是發展更廣泛，就難掌握。不同的程式語言，有它們各自的特點及強項，每個人時間都有限，無法全部學會，但至少要精通一項程式語言。\r\n\r\n\r\n(8)\r\nAnonymous: P18, 關鍵字誰訂?\r\nhmwu: 此問題應該是在問講義S4的第18頁「Text Analysis of Trump’s Tweets with R Code」? 完整問題，應該是「文字探勘(text mining)中，如何定義關鍵字?」。文字探勘(Text Mining)領域中，有所謂詞庫(Corpus)、詞典(Lexicon)、停用詞列表(Stop Words List)、情感詞典(Sentiment Lexicon)、同義詞詞典和反義詞詞典(Thesaurus)，和本體論(Ontology)等等，裡面會預先定義一些詞來表達語義。當然，還有其它方式來定義關鍵字，例如: 做詞頻分析選擇詞頻高的詞為關鍵字、計算TF-IDF選擇區分高的詞為關鍵字、利用NLP技術識別出文本中的命名實體為關鍵字、進行詞彙網絡關係分析找出密切相關的關鍵字、或分析者依照領域專家建議的自定詞也可以為關鍵字。','AM131台北19期_經理人研修班 #0330','','inherit','closed','closed','','3918-revision-v1','','','2024-04-02 14:05:00','2024-04-02 06:05:00','',3918,'https://mgr.aiacademy.tw/3918-revision-v1/',0,'revision','',0),(3920,124,'2024-04-02 14:06:55','2024-04-02 06:06:55','<h3>AM131-台北總校第十九期_經理人研修班 #第3週viedo</h3>\r\n\r\n<h4># 0330 資料科學與大數據分析 </h4>\r\n<iframe src=\"https://player.vimeo.com/video/929743778\" width=\"960\" height=\"540\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"></iframe>\r\n<br>\r\n\r\n<script src=\"https://player.vimeo.com/api/player.js\"></script>\r\n<br><br>\r\n<h3>吳漢銘老師 Slido_Q&A 答覆如下：</h3><br>\r\n\r\n(1)<br>\r\nAnonymous: 關於機器學習+深度學習與統計學相關的參考書，因為時間無法全數閱讀。若請老師只推1~2本的話，請問老師會推薦那本比較適合我們想更瞭解的入門學習者參考?<br>\r\nhmwu: 推薦以下兩本中文書:<br>\r\n(1) 機器學習的統計基礎：深度學習背後的核心技術，作者： 黃志勝，出版社：旗標，出版日期：2021/11/15。<br>\r\n(2) 深度學習的數學地圖：用 Python 實作神經網路的數學模型，作者： Masanori Akaishi ，譯者： 章奇煒, 王心薇，出版社：旗標，出版日期：2020/05/28。<br>\r\n<br>\r\n\r\n(2)<br>\r\nAnonymous: 是否所有數據都要設法標準化才能開始計算與預估呢？<br>\r\nhmwu: 基本上，不是所有的資料分析或機器學習方法都需要先做標準化(standardization)(或常規化normalization)，但在很多情況下，這一資料前置處理是建議要做的。<br>\r\n例如: (a) KNN, SVM, K-means這些方法對於資料的尺度(scale)非常敏感，未標準化的資料可能導致算法表現不佳。(b) 線性回歸、邏輯回歸、神經網絡等這些方法在找最佳解時，標準化後的資料可幫助加快梯度下降法的收斂速度，並提高訓練穩定性和。(c) 決策樹、隨機森林、梯度提升樹(GBM)等方法較不重視變數的具體數值，而是關注變數的排序和結構性分割點，此時資料標準化通常不是太必需的。<br>\r\n<br>\r\n\r\n(3)<br>\r\n佘祥麟: 上午第二節的17頁，政大的同記學能力: 16000總學生數，100人抽樣出來，那剩下的15900人如何歸類?<br>\r\nhmwu: 不太懂這個問題。但老師試著回答。若問題是想了解「政大學生的統計學能力」，那母體就是所有政大學生，總數是16000。因某些原因(成本、時間、技術)問題，無法全部一起同時舉辦一次會考，故會隨機採抽樣的方式，選取100人來舉辦考試，由這些學生(樣本)的平均成績來推論母體的平均成績。在進行隨機抽樣時，所選取的100人樣本用於估計或推論整個政治大學16000名學生群體的統計學能力。這100人樣本被視為整個群體的代表。在統計學中，我們假定這100人樣本是隨機選擇的，因此能夠在一定的信心水準下反映出全體學生的統計學能力，這100人樣本提供了對全體學生統計學能力的一個估計，也就是通過對抽樣出的100人進行分析，我們試圖推斷整個母體(包括這15900人)的統計特性。未被選中的15900人，作為群體的其餘部分，在分析中不直接被觀察或評估。理想情況下，如果抽樣過程是公正且隨機的，樣本的統計特性應該接近於整個群體的特性。因此，雖然這15900人沒有直接參與分析，他們仍然是研究結果推論和結論的一部分。<br>\r\n<br>\r\n\r\n(4)<br>\r\nAnonymous: 請問p. 16的「激活函數」是什麼意思？<br>\r\nhmwu: 深度學習中，激活函數(activation function)(或啟動函數)是一種數學函數，用於決定神經元是否應該被激活，它決定了神經元的輸出是否應該計入到下一層作為輸入。另外，激活函數的主要作用是截取到資料中的非線性關係到深度學習模型中，使得神經網絡能夠學習和執行更複雜的任務，如圖像識別、語音識別等。若沒有使用激活函數，無論神經網絡的層數有多少，最後的輸出都只是輸入的線性組合，這就會限制了神經網絡模型的預測能力。<br>\r\n<br>\r\n\r\n(5)<br>\r\nAnonymous: P1-8 standard error與standard deviation的差別在哪裡?<br>\r\nhmwu: 標準差(standard deviation)用來衡量一組資料點和它的平均數之間的離散程度，是描述單一資料集的分佈特性，亦即用於描述資料集內部的變異性。而標準誤(Standard error)是用來衡量一個樣本統計量(例如: 樣本平均數)對於母體參數(例如: 母體平均數)估計的精確度。以樣本平均數為例子，標準誤是衡量樣本平均數的可靠程度，而不是原始資料的離散程度。所以，標準誤是樣本平均值的標準差(這裡要想到老師講過的抽樣分佈概念)，它隨樣本大小的增加而減小。<br>\r\n<br>\r\n\r\n(6)<br>\r\n李忠穎: 感謝老師深入淺出的介紹！❤️<br>\r\nhmwu: 謝謝哦~~請記得給5星好評~ 感恩~<br>\r\n<br>\r\n\r\n(7)<br>\r\nAnonymous: 想請老師能分享對 Julia 語言的想法？<br>\r\nhmwu: 老師對Julia不清楚，但知道它是這時代新開發出來的程式語言，針對科學計算、大數據分析和機器學習領域具有高性能的表現。若是從事這方面的工作，可嚐試去學習，甚至專精，提昇自己較獨特的競爭力。因Julia 社群規模相較R/Python小，發展史較短，未來是否會更趨於小眾，還是發展更廣泛，就難掌握。不同的程式語言，有它們各自的特點及強項，每個人時間都有限，無法全部學會，但至少要精通一項程式語言。<br>\r\n<br>\r\n\r\n(8)<br>\r\nAnonymous: P18, 關鍵字誰訂?<br>\r\nhmwu: 此問題應該是在問講義S4的第18頁「Text Analysis of Trump’s Tweets with R Code」? 完整問題，應該是「文字探勘(text mining)中，如何定義關鍵字?」。文字探勘(Text Mining)領域中，有所謂詞庫(Corpus)、詞典(Lexicon)、停用詞列表(Stop Words List)、情感詞典(Sentiment Lexicon)、同義詞詞典和反義詞詞典(Thesaurus)，和本體論(Ontology)等等，裡面會預先定義一些詞來表達語義。當然，還有其它方式來定義關鍵字，例如: 做詞頻分析選擇詞頻高的詞為關鍵字、計算TF-IDF選擇區分高的詞為關鍵字、利用NLP技術識別出文本中的命名實體為關鍵字、進行詞彙網絡關係分析找出密切相關的關鍵字、或分析者依照領域專家建議的自定詞也可以為關鍵字。<br><br>','AM131台北19期_經理人研修班 #0330','','inherit','closed','closed','','3918-revision-v1','','','2024-04-02 14:06:55','2024-04-02 06:06:55','',3918,'https://mgr.aiacademy.tw/3918-revision-v1/',0,'revision','',0),(3921,124,'2024-04-02 14:08:16','2024-04-02 06:08:16','<h3>AM131-台北總校第十九期_經理人研修班 #第3週viedo</h3>\r\n\r\n<h4># 0330 資料科學與大數據分析 </h4>\r\n<iframe src=\"https://player.vimeo.com/video/929743778\" width=\"960\" height=\"540\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"></iframe>\r\n<br>\r\n\r\n<script src=\"https://player.vimeo.com/api/player.js\"></script>\r\n<br><br>\r\n<h3>吳漢銘老師 Slido_Q&A 答覆如下：</h3><br>\r\n\r\n(1)<br>\r\nAnonymous: 關於機器學習+深度學習與統計學相關的參考書，因為時間無法全數閱讀。若請老師只推1~2本的話，請問老師會推薦那本比較適合我們想更瞭解的入門學習者參考?<br>\r\n<strong>hmwu: </strong>推薦以下兩本中文書:<br>\r\n(1) 機器學習的統計基礎：深度學習背後的核心技術，作者： 黃志勝，出版社：旗標，出版日期：2021/11/15。<br>\r\n(2) 深度學習的數學地圖：用 Python 實作神經網路的數學模型，作者： Masanori Akaishi ，譯者： 章奇煒, 王心薇，出版社：旗標，出版日期：2020/05/28。<br>\r\n<br>\r\n\r\n(2)<br>\r\nAnonymous: 是否所有數據都要設法標準化才能開始計算與預估呢？<br>\r\n<strong>hmwu: </strong>基本上，不是所有的資料分析或機器學習方法都需要先做標準化(standardization)(或常規化normalization)，但在很多情況下，這一資料前置處理是建議要做的。<br>\r\n例如: (a) KNN, SVM, K-means這些方法對於資料的尺度(scale)非常敏感，未標準化的資料可能導致算法表現不佳。(b) 線性回歸、邏輯回歸、神經網絡等這些方法在找最佳解時，標準化後的資料可幫助加快梯度下降法的收斂速度，並提高訓練穩定性和。(c) 決策樹、隨機森林、梯度提升樹(GBM)等方法較不重視變數的具體數值，而是關注變數的排序和結構性分割點，此時資料標準化通常不是太必需的。<br>\r\n<br>\r\n\r\n(3)<br>\r\n佘祥麟: 上午第二節的17頁，政大的同記學能力: 16000總學生數，100人抽樣出來，那剩下的15900人如何歸類?<br>\r\n<strong>hmwu: </strong>不太懂這個問題。但老師試著回答。若問題是想了解「政大學生的統計學能力」，那母體就是所有政大學生，總數是16000。因某些原因(成本、時間、技術)問題，無法全部一起同時舉辦一次會考，故會隨機採抽樣的方式，選取100人來舉辦考試，由這些學生(樣本)的平均成績來推論母體的平均成績。在進行隨機抽樣時，所選取的100人樣本用於估計或推論整個政治大學16000名學生群體的統計學能力。這100人樣本被視為整個群體的代表。在統計學中，我們假定這100人樣本是隨機選擇的，因此能夠在一定的信心水準下反映出全體學生的統計學能力，這100人樣本提供了對全體學生統計學能力的一個估計，也就是通過對抽樣出的100人進行分析，我們試圖推斷整個母體(包括這15900人)的統計特性。未被選中的15900人，作為群體的其餘部分，在分析中不直接被觀察或評估。理想情況下，如果抽樣過程是公正且隨機的，樣本的統計特性應該接近於整個群體的特性。因此，雖然這15900人沒有直接參與分析，他們仍然是研究結果推論和結論的一部分。<br>\r\n<br>\r\n\r\n(4)<br>\r\nAnonymous: 請問p. 16的「激活函數」是什麼意思？<br>\r\n<strong>hmwu: </strong>深度學習中，激活函數(activation function)(或啟動函數)是一種數學函數，用於決定神經元是否應該被激活，它決定了神經元的輸出是否應該計入到下一層作為輸入。另外，激活函數的主要作用是截取到資料中的非線性關係到深度學習模型中，使得神經網絡能夠學習和執行更複雜的任務，如圖像識別、語音識別等。若沒有使用激活函數，無論神經網絡的層數有多少，最後的輸出都只是輸入的線性組合，這就會限制了神經網絡模型的預測能力。<br>\r\n<br>\r\n\r\n(5)<br>\r\nAnonymous: P1-8 standard error與standard deviation的差別在哪裡?<br>\r\n<strong>hmwu:</strong> 標準差(standard deviation)用來衡量一組資料點和它的平均數之間的離散程度，是描述單一資料集的分佈特性，亦即用於描述資料集內部的變異性。而標準誤(Standard error)是用來衡量一個樣本統計量(例如: 樣本平均數)對於母體參數(例如: 母體平均數)估計的精確度。以樣本平均數為例子，標準誤是衡量樣本平均數的可靠程度，而不是原始資料的離散程度。所以，標準誤是樣本平均值的標準差(這裡要想到老師講過的抽樣分佈概念)，它隨樣本大小的增加而減小。<br>\r\n<br>\r\n\r\n(6)<br>\r\n李忠穎: 感謝老師深入淺出的介紹！❤️<br>\r\n<strong>hmwu: </strong>謝謝哦~~請記得給5星好評~ 感恩~<br>\r\n<br>\r\n\r\n(7)<br>\r\nAnonymous: 想請老師能分享對 Julia 語言的想法？<br>\r\n<strong>hmwu: </strong>老師對Julia不清楚，但知道它是這時代新開發出來的程式語言，針對科學計算、大數據分析和機器學習領域具有高性能的表現。若是從事這方面的工作，可嚐試去學習，甚至專精，提昇自己較獨特的競爭力。因Julia 社群規模相較R/Python小，發展史較短，未來是否會更趨於小眾，還是發展更廣泛，就難掌握。不同的程式語言，有它們各自的特點及強項，每個人時間都有限，無法全部學會，但至少要精通一項程式語言。<br>\r\n<br>\r\n\r\n(8)<br>\r\nAnonymous: P18, 關鍵字誰訂?<br>\r\n<strong>hmwu: 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picture-in-picture; clipboard-write\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"大型語言模型實作初階班 (第三期) LLM-3A-4月17日\"></iframe></div><script src=\"https://player.vimeo.com/api/player.js\"></script>\r\n\r\n<div style=\"padding:51.63% 0 0 0;position:relative;\"><iframe src=\"https://player.vimeo.com/video/939017399?badge=0&amp;autopause=0&amp;player_id=0&amp;app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"大型語言模型實作初階班 (第三期) LLM-3A-4月24日\"></iframe></div><script src=\"https://player.vimeo.com/api/player.js\"></script>\r\n','大型語言模型實作初階班第三期課程表','','inherit','closed','closed','','3922-revision-v1','','','2024-04-25 17:07:35','2024-04-25 09:07:35','',3922,'https://mgr.aiacademy.tw/3922-revision-v1/',0,'revision','',0),(3957,76,'2024-04-25 17:10:18','2024-04-25 09:10:18','[aia_page_title]大型語言模型實作初階班第三期課程表[/aia_page_title]\r\n\r\n\r\n<div class=\"h2navwrap\"></div>\r\n<h2>2024/04/17~2024/04/24</h2> \r\n\r\n<p><strong> 請注意📢：該課程實作影片的回看期限為一個月至6月3日止⏰。\r\n我們亦提醒您，自結業起滿6個月後，將無法再登入學員專區。因此，若您需要下載任何講義或相關資料，請務必在此期限前完成。</strong></p>\r\n\r\n[week_course d=\"1rOZJ6Zm_Ygg_xW_Dpd8vm1yv4yhTRyye74QFFBS_8oc\" gid=\"1634844179\" show_place_mapping=1 bgcolor=\",,#adf,,#adf,,#adf,,#adf\"]\r\n\r\n\r\n\r\n[week_course_setup]\r\n<div style=\"padding:149.26% 0 0 0;position:relative;\"><iframe src=\"https://player.vimeo.com/video/936122898?badge=0&amp;autopause=0&amp;player_id=0&amp;app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"大型語言模型實作初階班 (第三期) LLM-3A-4月17日\"></iframe></div><script src=\"https://player.vimeo.com/api/player.js\"></script>\r\n\r\n<div style=\"padding:51.63% 0 0 0;position:relative;\"><iframe src=\"https://player.vimeo.com/video/939017399?badge=0&amp;autopause=0&amp;player_id=0&amp;app_id=58479\" 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style=\"padding:149.26% 0 0 0;position:relative;\"><iframe src=\"https://player.vimeo.com/video/936122898?badge=0&amp;autopause=0&amp;player_id=0&amp;app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"大型語言模型實作初階班 (第三期) LLM-3A-4月17日\"></iframe></div><script src=\"https://player.vimeo.com/api/player.js\"></script>\r\n\r\n<div style=\"padding:51.63% 0 0 0;position:relative;\"><iframe src=\"https://player.vimeo.com/video/939017399?badge=0&amp;autopause=0&amp;player_id=0&amp;app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"大型語言模型實作初階班 (第三期) LLM-3A-4月24日\"></iframe></div><script src=\"https://player.vimeo.com/api/player.js\"></script>\r\n','大型語言模型實作初階班第三期課程表','','inherit','closed','closed','','3922-revision-v1','','','2024-04-25 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05:23:38','<h3>AM131-台北總校第十九期_經理人研修班 #第6週viedo</h3>\r\n\r\n金`\r\n<br><br>','AM131台北19期_經理人研修班 #0427','','inherit','closed','closed','','3963-revision-v1','','','2024-05-02 13:23:38','2024-05-02 05:23:38','',3963,'https://mgr.aiacademy.tw/3963-revision-v1/',0,'revision','',0),(3965,124,'2024-05-02 13:25:22','2024-05-02 05:25:22','<h3>AM131-台北總校第十九期_經理人研修班 #第6週viedo</h3>\r\n\r\n<h4># 0427 手把手實作 </h4>\r\n<iframe src=\"https://player.vimeo.com/video/940234141\" width=\"960\" height=\"540\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"></iframe>\r\n<br>\r\n<script src=\"https://player.vimeo.com/api/player.js\"></script>\r\n<br><br>\r\n\r\n<h4># 0427 期中報告 </h4>\r\n<iframe src=\"https://player.vimeo.com/video/940012987\" width=\"960\" height=\"540\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"></iframe>\r\n<br>\r\n<script src=\"https://player.vimeo.com/api/player.js\"></script>\r\n<br><br>','AM131台北19期_經理人研修班 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01:37:39','',0,'https://mgr.aiacademy.tw/?p=3966',0,'post','',0),(3967,124,'2024-05-07 17:27:54','2024-05-07 09:27:54','<h3>AM131-台北總校第十九期_經理人研修班 #第7週viedo</h3>\r\n\r\n<h4># 0504 大型語言模型主題 </h4>\r\n<iframe src=\"https://player.vimeo.com/video/943574866\" width=\"960\" height=\"540\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"></iframe>\r\n<br>\r\n<script src=\"https://player.vimeo.com/api/player.js\"></script>\r\n<br><br>\r\n','AM131台北19期_經理人研修班 #0504','','inherit','closed','closed','','3966-revision-v1','','','2024-05-07 17:27:54','2024-05-07 09:27:54','',3966,'https://mgr.aiacademy.tw/3966-revision-v1/',0,'revision','',0),(3969,124,'2024-05-09 09:37:39','2024-05-09 01:37:39','<h3>AM131-台北總校第十九期_經理人研修班 #第7週viedo</h3>\r\n\r\n<h4># 0504 大型語言模型主題(概論、TAIDE) </h4>\r\n<iframe src=\"https://player.vimeo.com/video/943574866\" width=\"960\" height=\"540\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"></iframe>\r\n<br>\r\n<script src=\"https://player.vimeo.com/api/player.js\"></script>\r\n<br><br>\r\n<h4># 0504 大型語言模型主題(DaVinci) </h4>\r\n<iframe src=\"https://player.vimeo.com/video/944284906\" width=\"960\" height=\"540\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"></iframe>\r\n<br>\r\n<script src=\"https://player.vimeo.com/api/player.js\"></script>\r\n<br><br>\r\n','AM131台北19期_經理人研修班 #0504','','inherit','closed','closed','','3966-revision-v1','','','2024-05-09 09:37:39','2024-05-09 01:37:39','',3966,'https://mgr.aiacademy.tw/3966-revision-v1/',0,'revision','',0),(3975,76,'2024-05-10 10:44:18','2024-05-10 02:44:18','[aia_page_title]大型語言模型實作初階班第三期課程表[/aia_page_title]\r\n\r\n\r\n<div class=\"h2navwrap\"></div>\r\n<h2>2024/04/17~2024/04/24</h2> \r\n\r\n<p><strong style=\"color: darkgreen;\"> 請注意📢：該課程實作影片的回看期限為日止⏰。\r\n我們亦提醒您，自結業起滿6個月後，將無法再登入學員專區。因此，若您需要下載任何講義或相關資料，請務必在此期限前完成。</strong></p>\r\n\r\n[week_course d=\"1rOZJ6Zm_Ygg_xW_Dpd8vm1yv4yhTRyye74QFFBS_8oc\" gid=\"1634844179\" show_place_mapping=1 bgcolor=\",,#adf,,#adf,,#adf,,#adf\"]\r\n\r\n\r\n\r\n[week_course_setup]\r\n<div style=\"padding:149.26% 0 0 0;position:relative;\"><iframe src=\"https://player.vimeo.com/video/936122898?badge=0&amp;autopause=0&amp;player_id=0&amp;app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"大型語言模型實作初階班 (第三期) LLM-3A-4月17日\"></iframe></div><script src=\"https://player.vimeo.com/api/player.js\"></script>\r\n\r\n<div style=\"padding:51.63% 0 0 0;position:relative;\"><iframe src=\"https://player.vimeo.com/video/939017399?badge=0&amp;autopause=0&amp;player_id=0&amp;app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"大型語言模型實作初階班 (第三期) LLM-3A-4月24日\"></iframe></div><script src=\"https://player.vimeo.com/api/player.js\"></script>\r\n','大型語言模型實作初階班第三期課程表','','inherit','closed','closed','','3922-revision-v1','','','2024-05-10 10:44:18','2024-05-10 02:44:18','',3922,'https://mgr.aiacademy.tw/3922-revision-v1/',0,'revision','',0),(3984,115,'2024-05-10 12:21:36','2024-05-10 04:21:36','[aia_page_title]大型語言模型實作初階班第二期課程表[/aia_page_title]\r\n\r\n\r\n<div class=\"h2navwrap\"></div>\r\n<h2>2024/02/24~2024/03/02</h2>\r\n\r\n[week_course d=\"1b9l5z13pR5ktu6CjwUOidxJv6F81-G5whjiwJscEARY\" gid=\"1634844179\" show_place_mapping=1 bgcolor=\",,#adf,,#adf,,#adf,,#adf\"]\r\n\r\n\r\n\r\n','大型語言模型實作初階班第二期課程表','','inherit','closed','closed','','3862-revision-v1','','','2024-05-10 12:21:36','2024-05-10 04:21:36','',3862,'https://mgr.aiacademy.tw/3862-revision-v1/',0,'revision','',0),(3990,76,'2024-05-10 13:57:58','2024-05-10 05:57:58','[aia_page_title]大型語言模型實作初階班第三期課程表[/aia_page_title]\r\n\r\n\r\n<div class=\"h2navwrap\"></div>\r\n<h2>2024/04/17~2024/04/24</h2> 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allowfullscreen=\"allowfullscreen\"></iframe>\r\n<br>\r\n<script src=\"https://player.vimeo.com/api/player.js\"></script>\r\n<br><br>\r\n<h4># 0629 成果發表 & 結業式 </h4>\r\n<iframe src=\"https://player.vimeo.com/video/974900736\" width=\"960\" height=\"1200\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"></iframe>\r\n<br>\r\n<script src=\"https://player.vimeo.com/api/player.js\"></script>\r\n<br><br>','AM131台北19期_經理人研修班 #0629','','publish','open','open','','mgr-am131-viedo_w14','','','2024-07-03 16:08:16','2024-07-03 08:08:16','',0,'https://mgr.aiacademy.tw/?p=4082',0,'post','',0),(4083,124,'2024-07-03 15:13:42','2024-07-03 07:13:42','<h3>AM131-台北總校第十九期_經理人研修班 #第14週viedo</h3>\r\n\r\n<h4># 0629 專題演講 & 課程回顧 </h4>\r\n<iframe src=\"https://player.vimeo.com/video/974866380\" width=\"960\" height=\"540\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"></iframe>\r\n<br>\r\n<script src=\"https://player.vimeo.com/api/player.js\"></script>\r\n<br><br>\r\n<h4># 0629 成果發表 & 結業式 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