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Position: 吳俊逸 > AI
by 吳俊逸 2021-09-11 11:59:38, Reply(0), Views(87)
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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by 吳俊逸 2021-03-10 23:15:27, Reply(0), Views(860)
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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by 吳俊逸 2020-06-08 00:00:35, Reply(0), Views(815)
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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by 吳俊逸 2020-05-10 22:28:24, Reply(0), Views(469)
以下,將展示如何使用Orange3來顯示一組與所選參考圖像最相似的圖像。 Orange3將使用以下工作流程:
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by 吳俊逸 2020-05-07 12:19:07, Reply(0), Views(548)
Ref: https://thenewstack.io/when-holt-winters-is-better-than-machine-learning/Machine Learning (ML) gets a lot of hype, but its classical predecessors are still immensely powerful, especially in the time-series space. Error, Trend, Seasonality Forecast (ETS), Autoregressive Integrated Moving Average (ARIMA) and Holt-Winters are three Classical methods that are not only incredibly popular but are also excellent time-series predictors.
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by 吳俊逸 2020-04-28 22:23:46, Reply(0), Views(392)
Q1: When we extract the data and found that there is an error in the data and we disposing the data, do we still count this data as one of the null sample or considering this as an outlier? or just ditch it completely?
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by 吳俊逸 2020-04-28 22:21:16, Reply(0), Views(474)
Q1: Could you please give me an example to consider that which model should be applied when we want to predict downtime of the machine, when we have only the data collected from sensors which installed on that machine?
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by 吳俊逸 2020-04-28 22:19:46, Reply(0), Views(413)
Q1: Why a finer threshold (0.99) could not find one of the 2-humped camel?
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by 吳俊逸 2020-04-28 22:18:46, Reply(0), Views(368)
Q1: How long do you expect the AI to become more commercialized  and hence lowering the price for general company?
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by 吳俊逸 2020-04-28 22:16:39, Reply(0), Views(316)
Q1: What is the difference between data mining and machine learning?
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