Machine learningArtificial neural networkPhase equilibrium calculationPhase stability testPhase splitting calculationCompositional reservoir simulationTo accurately describe the fluid phase behaviour in reservoir simulation, Equation-of-State-based compositional models are usually used. However, phase equilibrium ...
aPractical application of learning content 学会内容的实际应用[translate]
knowledge of key aspects of deep and machine learning techniques in a practical, easy and fun way. The course provides students with practical hands-on experience in training deep and machine learning models using real-world dataset. This course covers several technique in a practical manner, the ...
onexperienceofbuildingamachinelearningsolutioninR.Next,usingRpackagessuchasrpart,randomforest,andmultipleimputationbychainedequations(MICE),youwilllearntoimplementalgorithmsincludingneuralnetclassifier,decisiontrees,andlinearandnon-linearregression.Asyouprogressthroughthebook,you’lldelveintovariousmachinelearningtechniques...
Continual learningWith our CI/CD workflow in place to deploy our application, we can now focus on continually improving our model. It becomes really easy to extend on this foundation to connect to scheduled runs (cron), data pipelines, drift detected through monitoring, online evaluation, etc. ...
The lack of parallel processing in machine learning tasks inhibits economy of performance, yet it may very well be worth the trouble. Read on for an introductory overview to GPU-based parallelism, the CUDA framework, and some thoughts on practical implem
practicalAI https://learnpracticalai.com https://github.com/GokuMohandas/practicalAI 发布于 2018-12-19 06:38 深度学习(Deep Learning) 机器学习 计算机科学 写下你的评论... 打开知乎App 在「我的页」右上角打开扫一扫 其他扫码方式:微信 下载知乎App ...
This document attempts to develop a curated list of Machine Learning resources, including books, papers, software, libraries, notebooks, etc. Most of the libraries are for Python though the rest of the materials here are generally suited for working with data. ...
In this session, we will discuss how analytics, AI and ML are being used in the finance industry to drive faster insight and value in supporting investment strategies, electronic transactions workflows, and line of business customer demand.
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