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https://github.com/ctgk/prml

PRML algorithms implemented in Python
https://github.com/ctgk/prml

jupyter notebook prml python

Last synced: about 11 hours ago
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PRML algorithms implemented in Python

Host: GitHub
URL: https://github.com/ctgk/prml
Owner: ctgk
License: mit
Created: 2017-02-05T12:02:58.000Z (over 7 years ago)
Default Branch: main
Last Pushed: 2024-09-12T12:08:08.000Z (13 days ago)
Last Synced: 2024-09-23T21:31:33.655Z (1 day ago)
Topics: jupyter, notebook, prml, python
Language: Jupyter Notebook
Homepage:
Size: 25.6 MB
Stars: 11,397
Watchers: 418
Forks: 3,256
Open Issues: 17
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE

Awesome Lists containing this project

README

        # PRML

Python codes implementing algorithms described in Bishop's book "Pattern Recognition and Machine Learning"

## Required Packages

- python 3

- numpy

- scipy

- jupyter (optional: to run jupyter notebooks)

- matplotlib (optional: to plot results in the notebooks)

- sklearn (optional: to fetch data)

## Notebooks

The notebooks in this repository can be viewed with nbviewer or other tools, or you can use [Amazon SageMaker Studio Lab](https://studiolab.sagemaker.aws/), a free computing environment on AWS (prior [registration with an email address](https://studiolab.sagemaker.aws/requestAccount) is required. Please refer to [this document](https://docs.aws.amazon.com/sagemaker/latest/dg/studio-lab-onboard.html) for usage).

From the table below, you can open the notebooks for each chapter in each of these environments.

|nbviewer|Amazon SageMaker Studio Lab|

|:-------|:--------------------------:|

|[ch1. Introduction](https://nbviewer.jupyter.org/github/ctgk/PRML/blob/main/notebooks/ch01_Introduction.ipynb)|[![Open in SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/ctgk/PRML/blob/main/notebooks/ch01_Introduction.ipynb)|

|[ch2. Probability Distributions](https://nbviewer.jupyter.org/github/ctgk/PRML/blob/main/notebooks/ch02_Probability_Distributions.ipynb)|[![Open in SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/ctgk/PRML/blob/main/notebooks/ch02_Probability_Distributions.ipynb)|

|[ch3. Linear Models for Regression](https://nbviewer.jupyter.org/github/ctgk/PRML/blob/main/notebooks/ch03_Linear_Models_for_Regression.ipynb)|[![Open in SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/ctgk/PRML/blob/main/notebooks/ch03_Linear_Models_for_Regression.ipynb)|

|[ch4. Linear Models for Classification](https://nbviewer.jupyter.org/github/ctgk/PRML/blob/main/notebooks/ch04_Linear_Models_for_Classfication.ipynb)|[![Open in SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/ctgk/PRML/blob/main/notebooks/ch04_Linear_Models_for_Classfication.ipynb)|

|[ch5. Neural Networks](https://nbviewer.jupyter.org/github/ctgk/PRML/blob/main/notebooks/ch05_Neural_Networks.ipynb)|[![Open in SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/ctgk/PRML/blob/main/notebooks/ch05_Neural_Networks.ipynb)|

|[ch6. Kernel Methods](https://nbviewer.jupyter.org/github/ctgk/PRML/blob/main/notebooks/ch06_Kernel_Methods.ipynb)|[![Open in SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/ctgk/PRML/blob/main/notebooks/ch06_Kernel_Methods.ipynb)|

|[ch7. Sparse Kernel Machines](https://nbviewer.jupyter.org/github/ctgk/PRML/blob/main/notebooks/ch07_Sparse_Kernel_Machines.ipynb)|[![Open in SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/ctgk/PRML/blob/main/notebooks/ch07_Sparse_Kernel_Machines.ipynb)|

|[ch8. Graphical Models](https://nbviewer.jupyter.org/github/ctgk/PRML/blob/main/notebooks/ch08_Graphical_Models.ipynb)|[![Open in SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/ctgk/PRML/blob/main/notebooks/ch08_Graphical_Models.ipynb)|

|[ch9. Mixture Models and EM](https://nbviewer.jupyter.org/github/ctgk/PRML/blob/main/notebooks/ch09_Mixture_Models_and_EM.ipynb)|[![Open in SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/ctgk/PRML/blob/main/notebooks/ch09_Mixture_Models_and_EM.ipynb)|

|[ch10. Approximate Inference](https://nbviewer.jupyter.org/github/ctgk/PRML/blob/main/notebooks/ch10_Approximate_Inference.ipynb)|[![Open in SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/ctgk/PRML/blob/main/notebooks/ch10_Approximate_Inference.ipynb)|

|[ch11. Sampling Methods](https://nbviewer.jupyter.org/github/ctgk/PRML/blob/main/notebooks/ch11_Sampling_Methods.ipynb)|[![Open in SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/ctgk/PRML/blob/main/notebooks/ch11_Sampling_Methods.ipynb)|

|[ch12. Continuous Latent Variables](https://nbviewer.jupyter.org/github/ctgk/PRML/blob/main/notebooks/ch12_Continuous_Latent_Variables.ipynb)|[![Open in SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/ctgk/PRML/blob/main/notebooks/ch12_Continuous_Latent_Variables.ipynb)|

|[ch13. Sequential Data](https://nbviewer.jupyter.org/github/ctgk/PRML/blob/main/notebooks/ch13_Sequential_Data.ipynb)|[![Open in SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/ctgk/PRML/blob/main/notebooks/ch13_Sequential_Data.ipynb)|

If you use the SageMaker Studio Lab, open a terminal and execute the following commands to install the required libraries.

```bash

conda env create -f environment.yaml  # might be optional

conda activate prml

python setup.py install

```