GitHub - kevinzhangftw/Hyperparameter-Tuning-Scikit-Learn-vs-GCP: A study on machine learning Hyperparameter Tuning on KNN SVN and custom NN using scikit learn and Google cloud platform

Beyond Scikit-Learn,

pip3 install scikit-optimize

now, you can run skopt-bayesian-and-random.ipynb

For keras, it will take a while to configure

You need tensorflow enabled in your kernel

pip3 install tensorflow

Even then, getting kernel to work is problem and a long time to configure

pip3 install keras

now you can run in Jonportion folder, you can run keras-GS-With-Layers.ipynb

That's all you need

Name		Name	Last commit message	Last commit date
Latest commit History 16 Commits
.ipynb_checkpoints		.ipynb_checkpoints
JonPortion		JonPortion
__pycache__		__pycache__
.DS_Store		.DS_Store
.gitignore		.gitignore
Color Predict Random Search.ipynb		Color Predict Random Search.ipynb
ColorBayes.ipynb		ColorBayes.ipynb
ColorPredictGridSearch.ipynb		ColorPredictGridSearch.ipynb
Colour.ipynb		Colour.ipynb
KerasGSWithLayers.ipynb		KerasGSWithLayers.ipynb
README.md		README.md
Tensorflow test.ipynb		Tensorflow test.ipynb
Weather City.ipynb		Weather City.ipynb
colour-data.csv		colour-data.csv
colour_bayes.py		colour_bayes.py
colour_data.py		colour_data.py
colour_predict.py		colour_predict.py
labels.csv		labels.csv
monthly-data-labelled.csv		monthly-data-labelled.csv
monthly-data-unlabelled.csv		monthly-data-unlabelled.csv
sample-labels.csv		sample-labels.csv
skopt.ipynb		skopt.ipynb
weather_data.py		weather_data.py
xAPI-Edu-Data.csv		xAPI-Edu-Data.csv

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