bonus

Sebastian Raschka, 2015

Python Machine Learning - Code Examples

Bonus Material

A collection of additional notebooks and code examples to clarify and explain concepts based on reader feedback.

A Basic Pipeline and Grid Search Setup [GitHub ipynb] [nbviewer]
An Extended Nested Cross-Validation Example [GitHub ipynb] [nbviewer]
A Simple(r) Barebones Flask Webapp Template [view directory][download as zip-file]
Reading handwritten digits from MNIST into NumPy arrays [GitHub ipynb] [nbviewer]
Scikit-learn Model Persistence using JSON [GitHub ipynb] [nbviewer]
Multinomial logistic regression / softmax regression [GitHub ipynb] [nbviewer]

Name	Name	Last commit message	Last commit date
parent directory ..
flask_webapp_ex01	flask_webapp_ex01
images	images
sckit-model-to-json	sckit-model-to-json
README.md	README.md
logistic_regression.ipynb	logistic_regression.ipynb
nested_cross_validation.ipynb	nested_cross_validation.ipynb
reading_mnist.ipynb	reading_mnist.ipynb
scikit-model-to-json.ipynb	scikit-model-to-json.ipynb
softmax-regression.ipynb	softmax-regression.ipynb
svm_iris_pipeline_and_gridsearch.ipynb	svm_iris_pipeline_and_gridsearch.ipynb