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

清真寺

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.

多產(chǎn)魚(yú)

Fulminate

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

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.

條街道往前推

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

Emmenagogue

Leszek J. Chmielewski,Arkadiusz Or?owski(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.

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fetter

978-1-4842-7761-4Tshepo Chris Nokeri 2022

hypotension

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.