Titlebook: Data Science Solutions with Python; Fast and Scalable Mo Tshepo Chris Nokeri Book 2022 Tshepo Chris Nokeri 2022 Big Data Analytics.Machine

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書目名稱Data Science Solutions with Python影響因子(影響力)




書目名稱Data Science Solutions with Python影響因子(影響力)學(xué)科排名




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書目名稱Data Science Solutions with Python網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Data Science Solutions with Python被引頻次




書目名稱Data Science Solutions with Python被引頻次學(xué)科排名




書目名稱Data Science Solutions with Python年度引用




書目名稱Data Science Solutions with Python年度引用學(xué)科排名




書目名稱Data Science Solutions with Python讀者反饋




書目名稱Data Science Solutions with Python讀者反饋學(xué)科排名

OGLE

oduces DL and an artificial neural network known as the Multilayer Perceptron (MLP) classifier. A way of performing cluster analysis using the K-Means model is covered. Dimension reduction techniques such as Pr978-1-4842-7761-4978-1-4842-7762-1

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and scaling them.Bridges the gap between machine and deep lApply supervised and unsupervised learning to solve practical and real-world big data problems. This book teaches you how to engineer features, optimize hyperparameters, train and test models, develop pipelines, and automate the machine lea

破譯密碼

Leszek J. Chmielewski,Arkadiusz Or?owskiected, manipulated, and examined using resilient and fault-tolerant technologies. It discusses the Scikit-Learn, Spark MLlib, and XGBoost frameworks. It also covers a deep learning framework called Keras. It concludes by discussing effective ways of setting up and managing these frameworks.

B-cell

Big Data, Machine Learning, and Deep Learning Frameworks,ected, manipulated, and examined using resilient and fault-tolerant technologies. It discusses the Scikit-Learn, Spark MLlib, and XGBoost frameworks. It also covers a deep learning framework called Keras. It concludes by discussing effective ways of setting up and managing these frameworks.

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姑姑在炫耀

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小淡水魚

https://doi.org/10.1007/978-3-031-00978-5This introductory chapter explains the ordinary least-squares method and executes it with the main Python frameworks (i.e., Scikit-Learn, Spark MLlib, and H2O). It begins by explaining the underlying concept behind the method.