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Some IPython notebooks based on Bishop's "Pattern Recognition and Machine Learning" book
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misc
1.1-polycurve.ipynb
1.2.5-curve-fitting-revisited.ipynb
2.1-binary-variables.ipynb
2.1.1-beta-distribution.ipynb
2.2-multinomial-variables.ipynb
2.5.1-kernel-density-estimators.ipynb
2.5.2-nearest-neighbor-classifiers.ipynb
3.1-4.1.1-4.1.3-linear-basis-function-models.ipynb
3.3.1-parameter-distribution.ipynb
4.1.1-discriminant-functions-two-classes.ipynb
4.3_Logistic_Regression.ipynb
5.1-feed-forward.ipynb
5.2-network-training.ipynb
README.md

README.md

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

1.1 Polynomial Curve Fitting

1.2.5 Curve Fitting Revisited

2.1 Binary Variables

2.1.1 The Beta Distribution

2.2 Multinomial Variables

2.5.1 Kernel Density Estimators

2.5.2 Nearest Neighbours

3.3.1 Parameter Distribution

3.1, 4.1.1, 4.1.3 Linear Basis Function Models

4.1.1 Discriminant Functions - Two Classes

4.3 Logistic Regression

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