清真寺
发表于 2025-3-25 06:39:19
Nonlinear Modeling With Scikit-Learn, PySpark, and H2O,This chapter executes and appraises a nonlinear method for binary classification (called .) using a diverse set of comprehensive Python frameworks (i.e., Scikit-Learn, Spark MLlib, and H2O). To begin, it clarifies the underlying concept behind the sigmoid function.
多产鱼
发表于 2025-3-25 10:27:41
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Fulminate
发表于 2025-3-25 15:30:43
Neural Networks with Scikit-Learn, Keras, and H2O,This chapter executes and assesses nonlinear neural networks to address binary classification using a diverse set of comprehensive Python frameworks (i.e., Scikit-Learn, Keras, and H2O).
LIEN
发表于 2025-3-25 17:15:58
Cluster Analysis with Scikit-Learn, PySpark, and H2O,This chapter explains the . cluster method by implementing a diverse set of Python frameworks (i.e., Scikit-Learn, PySpark, and H2O). To begin, it clarifies how the method apportions values to clusters.
条街道往前推
发表于 2025-3-25 21:14:17
Principal Component Analysis with Scikit-Learn, PySpark, and H2O,This chapter executes a simple dimension reducer (a principal component method) by implementing a diverse set of Python frameworks (Scikit-Learn, PySpark, and H2O). To begin, it clarifies how the method computes components.
interlude
发表于 2025-3-26 01:44:15
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Emmenagogue
发表于 2025-3-26 04:44:45
Leszek J. Chmielewski,Arkadiusz Orłowski(ML) and deep learning (DL) frameworks useful for building scalable applications. After reading this chapter, you will understand how big data is collected, manipulated, and examined using resilient and fault-tolerant technologies. It discusses the Scikit-Learn, Spark MLlib, and XGBoost frameworks.
抱负
发表于 2025-3-26 10:33:11
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fetter
发表于 2025-3-26 13:49:28
978-1-4842-7761-4Tshepo Chris Nokeri 2022
hypotension
发表于 2025-3-26 17:43:51
Big Data, Machine Learning, and Deep Learning Frameworks,(ML) and deep learning (DL) frameworks useful for building scalable applications. After reading this chapter, you will understand how big data is collected, manipulated, and examined using resilient and fault-tolerant technologies. It discusses the Scikit-Learn, Spark MLlib, and XGBoost frameworks.