wehub-resource-sync
24 lines
1.7 KiB
Markdown
Executable File
24 lines
1.7 KiB
Markdown
Executable File
# PRML
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Python codes implementing algorithms described in Bishop's book "Pattern Recognition and Machine Learning"
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## Required Packages
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- python 3
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- numpy
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- scipy
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- jupyter (optional: to run jupyter notebooks)
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- matplotlib (optional: to plot results in the notebooks)
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- sklearn (optional: to fetch data)
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## Notebooks
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- [ch1. Introduction](https://nbviewer.jupyter.org/github/ctgk/PRML/blob/master/notebooks/ch01_Introduction.ipynb)
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- [ch2. Probability Distributions](https://nbviewer.jupyter.org/github/ctgk/PRML/blob/master/notebooks/ch02_Probability_Distributions.ipynb)
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- [ch3. Linear Models for Regression](https://nbviewer.jupyter.org/github/ctgk/PRML/blob/master/notebooks/ch03_Linear_Models_for_Regression.ipynb)
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- [ch4. Linear Models for Classification](https://nbviewer.jupyter.org/github/ctgk/PRML/blob/master/notebooks/ch04_Linear_Models_for_Classfication.ipynb)
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- [ch5. Neural Networks](https://nbviewer.jupyter.org/github/ctgk/PRML/blob/master/notebooks/ch05_Neural_Networks.ipynb)
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- [ch6. Kernel Methods](https://nbviewer.jupyter.org/github/ctgk/PRML/blob/master/notebooks/ch06_Kernel_Methods.ipynb)
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- [ch7. Sparse Kernel Machines](https://nbviewer.jupyter.org/github/ctgk/PRML/blob/master/notebooks/ch07_Sparse_Kernel_Machines.ipynb)
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- [ch9. Mixture Models and EM](https://nbviewer.jupyter.org/github/ctgk/PRML/blob/master/notebooks/ch09_Mixture_Models_and_EM.ipynb)
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- [ch10. Approximate Inference](https://nbviewer.jupyter.org/github/ctgk/PRML/blob/master/notebooks/ch10_Approximate_Inference.ipynb)
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- [ch11. Sampling Methods](https://nbviewer.jupyter.org/github/ctgk/PRML/blob/master/notebooks/ch11_Sampling_Methods.ipynb)
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- [ch12. Continuous Latent Variables](https://nbviewer.jupyter.org/github/ctgk/PRML/blob/master/notebooks/ch12_Continuous_Latent_Variables.ipynb)
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