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by 吳俊逸 2018-07-24 09:54:03, 回應(0), 人氣(72)
https://www.mobility-work.com/blog/5-corrective-and-preventive-maintenance-levels-you-need-learn-about
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by 吳俊逸 2018-07-24 09:50:56, 回應(0), 人氣(79)
https://www.kdnuggets.com/2018/07/comparison-top-6-python-nlp-libraries.html#.W1Yd5Eh7vcw.linkedin
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by 吳俊逸 2018-06-28 09:36:32, 回應(0), 人氣(109)
https://www.kdnuggets.com/2018/05/general-approaches-machine-learning-process.htmlI actually came across Guo's article by way of first watching a video of his on YouTube, which came recommended after an afternoon of going down the Google I/O 2018 video playlist rabbit hole. The post is the same content as the video, and so if interested one of the two resources will suffice.
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by 吳俊逸 2018-06-27 09:45:51, 回應(0), 人氣(106)
http://www.ineews.com/zh-tw/377640/何為預測性維護策略?簡而言之就是藉助演演算法分析檢測故障發生前的機械狀態,並預測故障發生的時間。除此之外,還能夠確定可延長機械使用壽命的主動性任務類型。預測性維護作為一個新興市場,因為維護策略從所謂的事後控制方式轉移到通過分析和啟用預測性維護來解決問題,無疑向我們展示了一個就發展潛力的市場。在這個市場中,IoT平台商、低成本的安全雲存儲廠商以及提供動態數據模型的分析供應商扮演著至關重要的角色,發揮著越來越大的作用。
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by 吳俊逸 2018-06-21 17:31:08, 回應(0), 人氣(87)
過去只要產品做的好,就不愁沒有銷路。但是製造業現在面臨的狀況大不相同,市場快速變化、競爭加劇,需求開始走向個人化、客製化,但相應的生產條件卻沒有辦法快速靈活應變,再加上勞動力缺乏的問題,一場席捲整個製造業的第四次工業革命撲面襲來,跟不上大潮必然被淘汰出局。台灣的製造業曾經撐起了一個世代的輝煌,尤其擅長大量生產和代工製造。面對這樣的全球革命,製造廠商們也積極應對,希望乘著4.0的大潮再創奇蹟。
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by 吳俊逸 2018-06-21 17:16:52, 回應(0), 人氣(106)
第四次工業革命的到來,為各個國家提供了發展和轉型的機遇,也面臨競爭力格局變化的挑戰,智慧製造成為各國競爭的新戰場。   Will Blockchain
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by 吳俊逸 2018-06-20 23:10:34, 回應(0), 人氣(84)
Ref from: https://intellipaat.com/blog/data-scientist-vs-data-analyst/Data Science has grown tremendously in this century that one has to search in which field it is not being used. Healthcare, cyber security, banking, online retail, finance, SEO, digital marketing, and many other fields use Data Science in their businesses. Do you know that Bank of America finds out which loan borrowers would most likely default on their payments with a very high accuracy rate? In the organizational setup, there are many roles in Data Science like Data Scientist and Data Analyst. Both of the roles may seem the same to you but keep reading as we clearly differentiate between the two.Comparison of skills required for Data Scientist and Data AnalystCriteriaData ScientistData AnalystLanguages requiredLanguages like Pig, Hive, Matlab, Scala, SAS, SQL, Python, R need to be learntLanguages like Python, HTML, Javascript, R, SQL need to be learntSpecializationData visualization and expounding business stories to other teams in the organizationSpecialization in data visualization tools like Qlikview, Tableau, MSBIHadoop expertiseStrong knowledge to work around distributed storage and computing frameworks like HadoopIt’s not really necessaryAI skillsMachine learning conceptsNot required 
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by 吳俊逸 2018-05-29 14:47:44, 回應(0), 人氣(160)
Training and Testingy_data=df['price']x_data=df.drop('price',axis=1) #drop price data in x datafrom sklearn.model_selection import train_test_splitx_train, x_test, y_train, y_test = train_test_spl
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by 吳俊逸 2018-05-23 14:16:46, 回應(0), 人氣(179)
建立 DataFrame DataFrame 用來處理結構化(Table like)的資料,有列索引與欄標籤的二維資料集,可以透過 Dictionary 或是 Array 來建立,但也可以利用外部的資料來讀取後來建立,例如: CSV 檔案、資料庫等等。DataFrame 的操作 ❖ 資料描述查看 可以透過下列方法查看目前資料的資訊
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by 吳俊逸 2018-05-20 10:43:54, 回應(0), 人氣(119)
Job Description
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