[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-70860":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":16,"stars7d":16,"stars30d":17,"stars90d":16,"forks30d":16,"starsTrendScore":16,"compositeScore":18,"rankGlobal":10,"rankLanguage":10,"license":19,"archived":20,"fork":20,"defaultBranch":21,"hasWiki":22,"hasPages":20,"topics":23,"createdAt":10,"pushedAt":10,"updatedAt":28,"readmeContent":29,"aiSummary":30,"trendingCount":16,"starSnapshotCount":16,"syncStatus":31,"lastSyncTime":32,"discoverSource":33},70860,"PRML","ctgk\u002FPRML","ctgk","PRML algorithms implemented in Python","",null,"Jupyter Notebook",11720,3214,415,17,0,8,50.8,"MIT License",false,"main",true,[24,25,26,27],"jupyter","notebook","prml","python","2026-06-11 04:04:38","# 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:\u002F\u002Fstudiolab.sagemaker.aws\u002F), a free computing environment on AWS (prior [registration with an email address](https:\u002F\u002Fstudiolab.sagemaker.aws\u002FrequestAccount) is required. Please refer to [this document](https:\u002F\u002Fdocs.aws.amazon.com\u002Fsagemaker\u002Flatest\u002Fdg\u002Fstudio-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:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fctgk\u002FPRML\u002Fblob\u002Fmain\u002Fnotebooks\u002Fch01_Introduction.ipynb)|[![Open in SageMaker Studio Lab](https:\u002F\u002Fstudiolab.sagemaker.aws\u002Fstudiolab.svg)](https:\u002F\u002Fstudiolab.sagemaker.aws\u002Fimport\u002Fgithub\u002Fctgk\u002FPRML\u002Fblob\u002Fmain\u002Fnotebooks\u002Fch01_Introduction.ipynb)|\n|[ch2. Probability Distributions](https:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fctgk\u002FPRML\u002Fblob\u002Fmain\u002Fnotebooks\u002Fch02_Probability_Distributions.ipynb)|[![Open in SageMaker Studio Lab](https:\u002F\u002Fstudiolab.sagemaker.aws\u002Fstudiolab.svg)](https:\u002F\u002Fstudiolab.sagemaker.aws\u002Fimport\u002Fgithub\u002Fctgk\u002FPRML\u002Fblob\u002Fmain\u002Fnotebooks\u002Fch02_Probability_Distributions.ipynb)|\n|[ch3. Linear Models for Regression](https:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fctgk\u002FPRML\u002Fblob\u002Fmain\u002Fnotebooks\u002Fch03_Linear_Models_for_Regression.ipynb)|[![Open in SageMaker Studio Lab](https:\u002F\u002Fstudiolab.sagemaker.aws\u002Fstudiolab.svg)](https:\u002F\u002Fstudiolab.sagemaker.aws\u002Fimport\u002Fgithub\u002Fctgk\u002FPRML\u002Fblob\u002Fmain\u002Fnotebooks\u002Fch03_Linear_Models_for_Regression.ipynb)|\n|[ch4. Linear Models for Classification](https:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fctgk\u002FPRML\u002Fblob\u002Fmain\u002Fnotebooks\u002Fch04_Linear_Models_for_Classfication.ipynb)|[![Open in SageMaker Studio Lab](https:\u002F\u002Fstudiolab.sagemaker.aws\u002Fstudiolab.svg)](https:\u002F\u002Fstudiolab.sagemaker.aws\u002Fimport\u002Fgithub\u002Fctgk\u002FPRML\u002Fblob\u002Fmain\u002Fnotebooks\u002Fch04_Linear_Models_for_Classfication.ipynb)|\n|[ch5. Neural Networks](https:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fctgk\u002FPRML\u002Fblob\u002Fmain\u002Fnotebooks\u002Fch05_Neural_Networks.ipynb)|[![Open in SageMaker Studio Lab](https:\u002F\u002Fstudiolab.sagemaker.aws\u002Fstudiolab.svg)](https:\u002F\u002Fstudiolab.sagemaker.aws\u002Fimport\u002Fgithub\u002Fctgk\u002FPRML\u002Fblob\u002Fmain\u002Fnotebooks\u002Fch05_Neural_Networks.ipynb)|\n|[ch6. Kernel Methods](https:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fctgk\u002FPRML\u002Fblob\u002Fmain\u002Fnotebooks\u002Fch06_Kernel_Methods.ipynb)|[![Open in SageMaker Studio Lab](https:\u002F\u002Fstudiolab.sagemaker.aws\u002Fstudiolab.svg)](https:\u002F\u002Fstudiolab.sagemaker.aws\u002Fimport\u002Fgithub\u002Fctgk\u002FPRML\u002Fblob\u002Fmain\u002Fnotebooks\u002Fch06_Kernel_Methods.ipynb)|\n|[ch7. Sparse Kernel Machines](https:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fctgk\u002FPRML\u002Fblob\u002Fmain\u002Fnotebooks\u002Fch07_Sparse_Kernel_Machines.ipynb)|[![Open in SageMaker Studio Lab](https:\u002F\u002Fstudiolab.sagemaker.aws\u002Fstudiolab.svg)](https:\u002F\u002Fstudiolab.sagemaker.aws\u002Fimport\u002Fgithub\u002Fctgk\u002FPRML\u002Fblob\u002Fmain\u002Fnotebooks\u002Fch07_Sparse_Kernel_Machines.ipynb)|\n|[ch8. Graphical Models](https:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fctgk\u002FPRML\u002Fblob\u002Fmain\u002Fnotebooks\u002Fch08_Graphical_Models.ipynb)|[![Open in SageMaker Studio Lab](https:\u002F\u002Fstudiolab.sagemaker.aws\u002Fstudiolab.svg)](https:\u002F\u002Fstudiolab.sagemaker.aws\u002Fimport\u002Fgithub\u002Fctgk\u002FPRML\u002Fblob\u002Fmain\u002Fnotebooks\u002Fch08_Graphical_Models.ipynb)|\n|[ch9. Mixture Models and EM](https:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fctgk\u002FPRML\u002Fblob\u002Fmain\u002Fnotebooks\u002Fch09_Mixture_Models_and_EM.ipynb)|[![Open in SageMaker Studio Lab](https:\u002F\u002Fstudiolab.sagemaker.aws\u002Fstudiolab.svg)](https:\u002F\u002Fstudiolab.sagemaker.aws\u002Fimport\u002Fgithub\u002Fctgk\u002FPRML\u002Fblob\u002Fmain\u002Fnotebooks\u002Fch09_Mixture_Models_and_EM.ipynb)|\n|[ch10. Approximate Inference](https:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fctgk\u002FPRML\u002Fblob\u002Fmain\u002Fnotebooks\u002Fch10_Approximate_Inference.ipynb)|[![Open in SageMaker Studio Lab](https:\u002F\u002Fstudiolab.sagemaker.aws\u002Fstudiolab.svg)](https:\u002F\u002Fstudiolab.sagemaker.aws\u002Fimport\u002Fgithub\u002Fctgk\u002FPRML\u002Fblob\u002Fmain\u002Fnotebooks\u002Fch10_Approximate_Inference.ipynb)|\n|[ch11. Sampling Methods](https:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fctgk\u002FPRML\u002Fblob\u002Fmain\u002Fnotebooks\u002Fch11_Sampling_Methods.ipynb)|[![Open in SageMaker Studio Lab](https:\u002F\u002Fstudiolab.sagemaker.aws\u002Fstudiolab.svg)](https:\u002F\u002Fstudiolab.sagemaker.aws\u002Fimport\u002Fgithub\u002Fctgk\u002FPRML\u002Fblob\u002Fmain\u002Fnotebooks\u002Fch11_Sampling_Methods.ipynb)|\n|[ch12. Continuous Latent Variables](https:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fctgk\u002FPRML\u002Fblob\u002Fmain\u002Fnotebooks\u002Fch12_Continuous_Latent_Variables.ipynb)|[![Open in SageMaker Studio Lab](https:\u002F\u002Fstudiolab.sagemaker.aws\u002Fstudiolab.svg)](https:\u002F\u002Fstudiolab.sagemaker.aws\u002Fimport\u002Fgithub\u002Fctgk\u002FPRML\u002Fblob\u002Fmain\u002Fnotebooks\u002Fch12_Continuous_Latent_Variables.ipynb)|\n|[ch13. Sequential Data](https:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fctgk\u002FPRML\u002Fblob\u002Fmain\u002Fnotebooks\u002Fch13_Sequential_Data.ipynb)|[![Open in SageMaker Studio Lab](https:\u002F\u002Fstudiolab.sagemaker.aws\u002Fstudiolab.svg)](https:\u002F\u002Fstudiolab.sagemaker.aws\u002Fimport\u002Fgithub\u002Fctgk\u002FPRML\u002Fblob\u002Fmain\u002Fnotebooks\u002Fch13_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","该项目是《模式识别与机器学习》一书中算法的Python实现。它通过Jupyter Notebook的形式，提供了从基础的概率分布到复杂的神经网络和核方法等章节的代码示例，利用numpy、scipy等库实现了书中的关键算法，并支持可视化结果展示。此项目适合对机器学习理论有深入理解需求的研究者、学生以及从业者使用，尤其适用于那些希望通过实践来加深对PRML中概念和技术理解的人群。",2,"2026-06-11 03:34:37","high_star"]