Hyperparameter Tuning with River
24
HPT: River
Hyperparameter Tuning Cookbook
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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
23
Step 2: Initialization of the Empty
fun_control
Dictionary
Hyperparameter Tuning with River
24
HPT: River
25
Simplifying Hyperparameter Tuning in Online Machine Learning—The spotRiverGUI
26
river
Hyperparameter Tuning: Hoeffding Tree Regressor with Friedman Drift Data
27
The Friedman Drift Data Set
Hyperparameter Tuning with PyTorch Lightning
28
Basic Lightning Module
29
Details of the Lightning Module Integration in spotpython
30
User Specified Basic Lightning Module With spotpython
31
HPT PyTorch Lightning: Data
32
Hyperparameter Tuning with
spotpython
and
PyTorch
Lightning for the Diabetes Data Set
33
Hyperparameter Tuning with PyTorch Lightning and User Data Sets
34
Hyperparameter Tuning with PyTorch Lightning and User Models
35
Hyperparameter Tuning with PyTorch Lightning: ResNets
36
Neural ODEs
37
Neural ODE Example
38
Physics Informed Neural Networks
39
Hyperparameter Tuning with PyTorch Lightning: Physics Informed Neural Networks
40
Explainable AI with SpotPython and Pytorch
41
HPT PyTorch Lightning Transformer: Introduction
42
Hyperparameter Tuning of a Transformer Network with PyTorch Lightning
43
Saving and Loading
44
Hyperparameter Tuning with
spotpython
and
PyTorch
Lightning for the Diabetes Data Set Using a ResNet Model
45
Hyperparameter Tuning with
spotpython
and
PyTorch
Lightning for the Diabetes Data Set Using a User Specified ResNet Model
46
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
24.1
Introduction to River
Hyperparameter Tuning with River
24
HPT: River
24
HPT: River
24.1
Introduction to River
23
Step 2: Initialization of the Empty
fun_control
Dictionary
25
Simplifying Hyperparameter Tuning in Online Machine Learning—The spotRiverGUI