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Title: GitHub - codehxj/PRML: PRML algorithms implemented in Python

Open Graph Title: GitHub - codehxj/PRML: PRML algorithms implemented in Python

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Description: PRML algorithms implemented in Python. Contribute to codehxj/PRML development by creating an account on GitHub.

Open Graph Description: PRML algorithms implemented in Python. Contribute to codehxj/PRML development by creating an account on GitHub.

X Description: PRML algorithms implemented in Python. Contribute to codehxj/PRML development by creating an account on GitHub.

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ch1. Introductionhttps://nbviewer.jupyter.org/github/ctgk/PRML/blob/master/notebooks/ch01_Introduction.ipynb
ch2. Probability Distributionshttps://nbviewer.jupyter.org/github/ctgk/PRML/blob/master/notebooks/ch02_Probability_Distributions.ipynb
ch3. Linear Models for Regressionhttps://nbviewer.jupyter.org/github/ctgk/PRML/blob/master/notebooks/ch03_Linear_Models_for_Regression.ipynb
ch4. Linear Models for Classificationhttps://nbviewer.jupyter.org/github/ctgk/PRML/blob/master/notebooks/ch04_Linear_Models_for_Classfication.ipynb
ch5. Neural Networkshttps://nbviewer.jupyter.org/github/ctgk/PRML/blob/master/notebooks/ch05_Neural_Networks.ipynb
ch6. Kernel Methodshttps://nbviewer.jupyter.org/github/ctgk/PRML/blob/master/notebooks/ch06_Kernel_Methods.ipynb
ch7. Sparse Kernel Machineshttps://nbviewer.jupyter.org/github/ctgk/PRML/blob/master/notebooks/ch07_Sparse_Kernel_Machines.ipynb
ch9. Mixture Models and EMhttps://nbviewer.jupyter.org/github/ctgk/PRML/blob/master/notebooks/ch09_Mixture_Models_and_EM.ipynb
ch10. Approximate Inferencehttps://nbviewer.jupyter.org/github/ctgk/PRML/blob/master/notebooks/ch10_Approximate_Inference.ipynb
ch11. Sampling Methodshttps://nbviewer.jupyter.org/github/ctgk/PRML/blob/master/notebooks/ch11_Sampling_Methods.ipynb
ch12. Continuous Latent Variableshttps://nbviewer.jupyter.org/github/ctgk/PRML/blob/master/notebooks/ch12_Continuous_Latent_Variables.ipynb
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