Hyperparameter Tuning for Machine and Deep Learning with R: A Practical Guide

Eva Bartz, Thomas Bartz-Beielstein, Martin Zaefferer, Olaf Mersmann ★ 3.4/5 · ItemOracle Score Mid-Range

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Hyperparameter Tuning for Machine and Deep Learning with R: A Practical Guide

Guide to practical hyperparameter tuning in machine and deep learning using R. Focuses on actionable techniques and real-world applications. Customer insight highlights interest in practical methods

Highlights

  • practical hyperparameter tuning methods
  • machine and deep learning in R
  • multi-author expertise

Pros

  • practical hyperparameter tuning guidance
  • covers machine and deep learning with R
  • clear, actionable techniques
  • authored by multiple experts

Cons

  • no features section available
  • customer insights are None
  • title lengthy for compact displays

Best For

  • tuning models in R-based ML workflows
  • optimizing deep learning hyperparameters
  • experimental protocol for parameter search
  • model performance improvement studies
  • academic or research projects in ML
  • practical guide for data scientists

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