Hyperparameter Tuning with River
18
HPT: River
Hyperparameter Tuning Cookbook
Twitter
LinkedIn
Preface
Optimization
1
Introduction: Optimization
2
Aircraft Wing Weight Example
3
Introduction to
scipy.optimize
4
Sequential Parameter Optimization: Using
scipy
Optimizers
Numerical Methods
5
Introduction: Numerical Methods
6
Kriging (Gaussian Process Regression)
7
Introduction to spotPython
8
Multi-dimensional Functions
9
Isotropic and Anisotropic Kriging
10
Using
sklearn
Surrogates in
spotPython
11
Sequential Parameter Optimization: Gaussian Process Models
12
Expected Improvement
13
Handling Noise
14
Optimal Computational Budget Allocation in
Spot
15
Kriging with Varying Correlation-p
Hyperparameter Tuning with Sklearn
16
HPT: sklearn
17
HPT: sklearn SVC on Moons Data
Hyperparameter Tuning with River
18
HPT: River
19
Simplifying Hyperparameter Tuning in Online Machine Learning—The spotRiverGUI
20
river
Hyperparameter Tuning: Hoeffding Adaptive Tree Regressor with Friedman Drift Data
21
river
Hyperparameter Tuning: Mondrian Tree Regressor with Friedman Drift Data
Hyperparameter Tuning with PyTorch Lightning
22
HPT PyTorch Lightning: Data
23
HPT PyTorch Lightning: Diabetes
24
HPT PyTorch Lightning: Diabetes Using a Recurrent Neural Network
25
HPT PyTorch Lightning: User Specified Data Set and Regression Model
26
Explainable AI with SpotPython and Pytorch
27
HPT PyTorch Lightning Transformer: Introduction
28
HPT PyTorch Lightning Transformer: Diabetes
29
Saving and Loading
Appendices
A
Introduction to Jupyter Notebook
B
Git Introduction
C
Python Introduction
D
Documentation of the Sequential Parameter Optimization
References
Table of contents
18.1
Introduction to River
Hyperparameter Tuning with River
18
HPT: River
18
HPT: River
18.1
Introduction to River
17
HPT: sklearn SVC on Moons Data
19
Simplifying Hyperparameter Tuning in Online Machine Learning—The spotRiverGUI