PRML IPython notebook demos

To play around with the notebooks you'll need to install IPython and clone the repo, but if you just want to view you can use the links below.

Use the following command in the repo directory to launch IPython and get the inline plots working

ipython notebook --pylab=inline

Links to view notebooks

Name		Name	Last commit message	Last commit date
Latest commit History 43 Commits
misc		misc
1.1-polycurve.ipynb		1.1-polycurve.ipynb
1.2.5-curve-fitting-revisited.ipynb		1.2.5-curve-fitting-revisited.ipynb
2.1-binary-variables.ipynb		2.1-binary-variables.ipynb
2.1.1-beta-distribution.ipynb		2.1.1-beta-distribution.ipynb
2.2-multinomial-variables.ipynb		2.2-multinomial-variables.ipynb
2.5.1-kernel-density-estimators.ipynb		2.5.1-kernel-density-estimators.ipynb
2.5.2-nearest-neighbor-classifiers.ipynb		2.5.2-nearest-neighbor-classifiers.ipynb
3.1-4.1.1-4.1.3-linear-basis-function-models.ipynb		3.1-4.1.1-4.1.3-linear-basis-function-models.ipynb
3.3.1-parameter-distribution.ipynb		3.3.1-parameter-distribution.ipynb
4.1.1-discriminant-functions-two-classes.ipynb		4.1.1-discriminant-functions-two-classes.ipynb
4.3_Logistic_Regression.ipynb		4.3_Logistic_Regression.ipynb
5.1-feed-forward.ipynb		5.1-feed-forward.ipynb
5.2-network-training.ipynb		5.2-network-training.ipynb
README.md		README.md