{"id":13415595,"url":"https://github.com/ctgk/PRML","last_synced_at":"2025-03-14T23:30:56.291Z","repository":{"id":37663436,"uuid":"80990040","full_name":"ctgk/PRML","owner":"ctgk","description":"PRML algorithms implemented in Python","archived":false,"fork":false,"pushed_at":"2024-09-27T10:22:23.000Z","size":26946,"stargazers_count":11433,"open_issues_count":17,"forks_count":3253,"subscribers_count":417,"default_branch":"main","last_synced_at":"2024-10-29T10:06:09.527Z","etag":null,"topics":["jupyter","notebook","prml","python"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ctgk.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":".github/FUNDING.yml","license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null},"funding":{"github":"ctgk"}},"created_at":"2017-02-05T12:02:58.000Z","updated_at":"2024-10-28T08:16:30.000Z","dependencies_parsed_at":"2024-09-11T12:31:43.368Z","dependency_job_id":"007a732a-e14c-4304-987c-ca6c2df2bb4a","html_url":"https://github.com/ctgk/PRML","commit_stats":{"total_commits":322,"total_committers":6,"mean_commits":"53.666666666666664","dds":"0.16770186335403725","last_synced_commit":"0aba5c8b12adf99d53f3471b7cf4fa117d64acc8"},"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ctgk%2FPRML","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ctgk%2FPRML/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ctgk%2FPRML/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ctgk%2FPRML/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ctgk","download_url":"https://codeload.github.com/ctgk/PRML/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243057326,"owners_count":20229174,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["jupyter","notebook","prml","python"],"created_at":"2024-07-30T21:00:50.598Z","updated_at":"2025-03-14T23:30:56.285Z","avatar_url":"https://github.com/ctgk.png","language":"Jupyter Notebook","readme":"# PRML\nPython codes implementing algorithms described in Bishop's book \"Pattern Recognition and Machine Learning\"\n\n## Required Packages\n- python 3\n- numpy\n- scipy\n- jupyter (optional: to run jupyter notebooks)\n- matplotlib (optional: to plot results in the notebooks)\n- sklearn (optional: to fetch data)\n\n## Notebooks\n\nThe 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).\n\nFrom the table below, you can open the notebooks for each chapter in each of these environments.\n\n|nbviewer|Amazon SageMaker Studio Lab|\n|:-------|:--------------------------:|\n|[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)|\n|[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)|\n|[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)|\n|[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)|\n|[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)|\n|[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)|\n|[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)|\n|[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)|\n|[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)|\n|[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)|\n|[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)|\n|[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)|\n|[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)|\n\nIf you use the SageMaker Studio Lab, open a terminal and execute the following commands to install the required libraries.\n\n```bash\nconda env create -f environment.yaml  # might be optional\nconda activate prml\npython setup.py install\n```\n","funding_links":["https://github.com/sponsors/ctgk"],"categories":["Jupyter Notebook","Machine Learning","其他_机器学习与深度学习","Studio Labで学べる教材"],"sub_categories":["2. Documentation","データサイエンス"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fctgk%2FPRML","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fctgk%2FPRML","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fctgk%2FPRML/lists"}