REF:https://ithelp.ithome.com.tw/articles/10276333Auto-sklearn 採用元學習 (Meta Learning) 選擇模型和超參數優化的方法作為搜尋最佳模型的重點。此 AutoML 套件主要是搜尋所有 Sklearn 機器學習演算法以模型的超參數，並使用貝葉斯優化 (Bayesian Optimization) 與自動整合 (Ensemble Selection) 的架構在有限時間內搜尋最佳的模型。第一版的 Auto-sklearn 於 2015 年發表在 NIPS(Neural Information Processing Systems) 會議上，論文名稱為 Efficient and Robust Automated Machine Learning。有別於其他的 AutoML 方法，Auto-sklearn 提出了元學習架構改善了貝葉斯優化在初始冷啟動的缺點，並提供一個好的採樣方向更快速尋找最佳的模型[1]。第二個版本於 2020 年發布，論文名稱為 Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning。在新的版本中修改了元學習架構，並不依賴元特徵來選擇模型選擇與調參策略。而是引入了一個元學習策略選擇器，根據資料集中的樣本數量和特徵，訂定了一個模型選擇的策略[3]。
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https://developer.nvidia.com/blog/new-on-ngc-catalog-samsung-sdss-brightrics-an-ai-accelerator-for-automating-and-accelerating-deep-learning-training/Samsung SDS Brightics, an AI Accelerator for Automating and Accelerating Deep Learning TrainingTraining AI models is an extremely time-consuming process. Without proper insight into a feasible alternative to time-consuming development and migration of model training to exploit the power of large, distributed clusters, training projects remain considerably long lasting. To address these issues, Samsung SDS developed the Brightics AI Accelerator. The Kubernetes-based, containerized application, is now available on the NVIDIA NGC catalog – a GPU-optimized hub for AI and HPC containers, pre-trained models, industry SDKs, and Helm charts that helps simplify and accelerate AI development and deployment processes.  
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ＲＥＦ：https://clarivate.com.tw/blog/2019/10/29/ten-hacks-to-unlock-the-value-of-patent-analytics如何分析海量增長的專利資訊，挖掘潛在價值？ 
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key: 1) model export as (pkcls) 2) data export as (tab) 3) import Orange (install Orange3) & pickletrain your model, and save the model as pkcls. Moreover, save your test data as ta
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Ref: https://www.appliedmaterials.com/ja/nanochip/nanochip-fab-solutions/july-2018/leveraging-the-digital-twin-in-smart-microelectronics-manufacturingBy James MoyneAmong the many tenets of smart manufacturing,[1] “digital twin” solutions represent a significant opportunity for microelectronics manufacturers to leverage existing and emerging technologies to improve quality and throughput, and reduce variability and cost.
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Ref:http://franktsao.blogspot.com/2009/10/apc.html曹永誠 [刊登於電機月刊第174期 (2005年6月)]1、前言以一座八吋晶圓廠來說，投資額約在200億~300億，而12吋晶圓
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https://www.lexalytics.com/lexablog/text-analytics-nlp-rpa-use-casesThis article explains everything you need to know about text analytics and natural language processing in robotic process automation. First, we define RPA and natural language processing, and explain how they fit together. Then we outline a number of trending text analytics use cases in RPA. Finally, we cite Forrester and Gartner to put these use cases in perspective and explain how the RPA market is changing, and where it’s going. As we demonstrate, the future of RPA is in better analytics and customization with larger, transformational use cases. To stay ahead, RPA vendors must improve their NLP capabilities.
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原文網址：https://kknews.cc/tech/38qm5l8.html
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REF: https://machinelearningmastery.com/how-to-get-started-with-deep-learning-for-time-series-forecasting-7-day-mini-course/Deep learning methods offer a lot of promise for time series forecasting, such as the automatic learning of temporal dependence and the automatic handling of temporal structures like trends and seasonality.
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Ref: https://www.kdnuggets.com/2019/12/mathworks-predictive-maintenance-theory-practice.html?s_v1=30212&elqem=2962332_EM_WW_20-05_NEWSLETTER_CG-DIGEST-DEFAULTThese days you keep hearing about predictive maintenance and how valuable it is. It’s the utopia where equipment operators can anticipate impending malfunctions, preemptively schedule repairs, minimize disruption to factory operation, and, most importantly, protect equipment from catastrophic failures. With bright eyes, we can all agree on the value of predictive maintenance, or should I say the expected value of predictive maintenance?
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