Hyperparameter Tuning with Sklearn
19
HPT: sklearn
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
Data-Driven Modeling and Optimization
16
Data-Driven Modeling and Optimization
Machine Learning and AI
17
Machine Learning and Artificial Intelligence
Introduction to Hyperparameter Tuning
18
Hyperparameter Tuning
Hyperparameter Tuning with Sklearn
19
HPT: sklearn
20
HPT: sklearn SVC on Moons Data
Hyperparameter Tuning with River
21
HPT: River
22
Simplifying Hyperparameter Tuning in Online Machine Learning—The spotRiverGUI
23
river
Hyperparameter Tuning: Hoeffding Tree Regressor with Friedman Drift Data
24
The Friedman Drift Data Set
Hyperparameter Tuning with PyTorch Lightning
25
HPT PyTorch Lightning: Data
26
Hyperparameter Tuning with
spotpython
and
PyTorch
Lightning for the Diabetes Data Set
27
Hyperparameter Tuning with PyTorch Lightning and User Data Sets
28
Hyperparameter Tuning with PyTorch Lightning and User Models
29
Hyperparameter Tuning with PyTorch Lightning: Physics Informed Neural Networks
30
Explainable AI with SpotPython and Pytorch
31
HPT PyTorch Lightning Transformer: Introduction
32
Hyperparameter Tuning of a Transformer Network with PyTorch Lightning
33
Saving and Loading
34
Hyperparameter Tuning with
spotpython
and
PyTorch
Lightning for the Diabetes Data Set Using a ResNet Model
35
Hyperparameter Tuning with
spotpython
and
PyTorch
Lightning for the Diabetes Data Set Using a User Specified ResNet Model
36
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
Solutions to Selected Exercises
References
Table of contents
19.1
Introduction to sklearn
Hyperparameter Tuning with Sklearn
19
HPT: sklearn
19
HPT: sklearn
19.1
Introduction to sklearn
18
Hyperparameter Tuning
20
HPT: sklearn SVC on Moons Data