Hyperparameter Tuning with River
23
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
16
Factorial Variables
17
User-Specified Functions: Extending the Analytical Class
Data-Driven Modeling and Optimization
18
Data-Driven Modeling and Optimization
Machine Learning and AI
19
Machine Learning and Artificial Intelligence
Introduction to Hyperparameter Tuning
20
Hyperparameter Tuning
Hyperparameter Tuning with Sklearn
21
HPT: sklearn
22
HPT: sklearn SVC on Moons Data
Hyperparameter Tuning with River
23
HPT: River
24
Simplifying Hyperparameter Tuning in Online Machine Learning—The spotRiverGUI
25
river
Hyperparameter Tuning: Hoeffding Tree Regressor with Friedman Drift Data
26
The Friedman Drift Data Set
Hyperparameter Tuning with PyTorch Lightning
27
Basic Lightning Module
28
Details of the Lightning Module Integration in spotpython
29
User Specified Basic Lightning Module With spotpython
30
HPT PyTorch Lightning: Data
31
Hyperparameter Tuning with
spotpython
and
PyTorch
Lightning for the Diabetes Data Set
32
Hyperparameter Tuning with PyTorch Lightning and User Data Sets
33
Hyperparameter Tuning with PyTorch Lightning and User Models
34
Hyperparameter Tuning with PyTorch Lightning: Physics Informed Neural Networks
35
Explainable AI with SpotPython and Pytorch
36
HPT PyTorch Lightning Transformer: Introduction
37
Hyperparameter Tuning of a Transformer Network with PyTorch Lightning
38
Saving and Loading
39
Hyperparameter Tuning with
spotpython
and
PyTorch
Lightning for the Diabetes Data Set Using a ResNet Model
40
Hyperparameter Tuning with
spotpython
and
PyTorch
Lightning for the Diabetes Data Set Using a User Specified ResNet Model
41
Hyperparameter Tuning with
spotpython
and
PyTorch
Lightning Using a CondNet Model
Appendices
A
Introduction to Jupyter Notebook
B
Git Introduction
C
Python Introduction
D
Documentation of the Sequential Parameter Optimization
E
Datasets
F
Using Slurm
G
Python Package Building
H
Solutions to Selected Exercises
References
Table of contents
23.1
Introduction to River
Hyperparameter Tuning with River
23
HPT: River
23
HPT: River
23.1
Introduction to River
22
HPT: sklearn SVC on Moons Data
24
Simplifying Hyperparameter Tuning in Online Machine Learning—The spotRiverGUI