SPOT

Publications

Hyperparameter-Tuning Cookbook

The Hyperparameter-Tuning Cookbook can be downloaded for free from: https://sequential-parameter-optimization.github.io/Hyperparameter-Tuning-Cookbook/.

Online Machine Learning: Eine praxisorientierte Einführung – 2. Auflage

• Overview:
  • Zeigt die Vorteile von Online Machine Learning gegenüber Batch Machine Learning
  • Bietet Wissen und Einschätzungen von erfahrenen Experten
  • Mit Quelltext und Beispielen für die Anwendung in der Praxis
• Mehr:
  • https://link.springer.com/book/10.1007/978-3-658-46162-1

Online Machine Learning: A Practical Guide with Examples in Python

• Overview:
  • Presents systematic comparison of OML and BML in terms of performance, time and memory requirements
  • Explains how OML can be customized by hyperparameter tuning
  • Accompanied with continuously-updated code and material in the GitHub repository
• More:
  • https://link.springer.com/book/10.1007/978-981-99-7007-0

Hyperparameter Tuning for Machine and Deep Learning in R – A Practical Guide

• Overview:
  • Provides hands-on examples that illustrate how hyperparameter tuning can be applied in industry and academia
  • Gives deep insights into the working mechanisms of machine learning and deep learning
  • This book is open access, which means that you have free and unlimited access
  • Includes code that equips readers to achieve better results with less time, costs, and effort
• More:
  • https://link.springer.com/book/10.1007/978-981-19-5170-1

Software

spotPython

• Documentation: https://sequential-parameter-optimization.github.io/spotPython/

spotRiver

• Documentation: https://sequential-parameter-optimization.github.io/spotRiver/