{"id":24936780,"url":"https://github.com/nagexiucai/mla","last_synced_at":"2026-05-03T19:32:09.149Z","repository":{"id":213609382,"uuid":"125953727","full_name":"nagexiucai/MLA","owner":"nagexiucai","description":"机器学习算法。","archived":false,"fork":false,"pushed_at":"2018-04-13T07:26:38.000Z","size":12546,"stargazers_count":3,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-03-28T16:43:40.713Z","etag":null,"topics":["ai","algorithm","deep-learning","machine-learning","nn","python","tutorial"],"latest_commit_sha":null,"homepage":"http://ai.nagexiucai.com","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/nagexiucai.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"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}},"created_at":"2018-03-20T03:12:30.000Z","updated_at":"2023-03-05T01:18:54.000Z","dependencies_parsed_at":"2023-12-22T00:41:52.458Z","dependency_job_id":null,"html_url":"https://github.com/nagexiucai/MLA","commit_stats":null,"previous_names":["nagexiucai/mla"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/nagexiucai/MLA","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nagexiucai%2FMLA","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nagexiucai%2FMLA/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nagexiucai%2FMLA/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nagexiucai%2FMLA/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/nagexiucai","download_url":"https://codeload.github.com/nagexiucai/MLA/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nagexiucai%2FMLA/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32582560,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-03T06:36:36.687Z","status":"ssl_error","status_checked_at":"2026-05-03T06:36:09.306Z","response_time":103,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"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":["ai","algorithm","deep-learning","machine-learning","nn","python","tutorial"],"created_at":"2025-02-02T16:57:13.975Z","updated_at":"2026-05-03T19:32:09.129Z","avatar_url":"https://github.com/nagexiucai.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# MLA\n机器学习算法。\n\n## 前言\n做编程八年，始终有一个点是自己不那么自信的，就是算法，因为数学的天花板限制了理解力。\n\n最近一次交流活动中，自己跟朋友说：\n\n\t编程从业者分为三大层次：编码人员、程序设计师、计算机科学家。\n\n自己的定位大概是“努力做到中高水平的程序设计师”，因为数学能力不足以支撑自己走得更高更远。\n\n去年以来爆发的人工智能（机器学习、深度学习）就是个算法集中领域，它焕发新生地革新着一切系统。\n\n回顾学习信号与系统、数字图像处理、随机过程等课程以及应用OpenCV等库的经历，觉得很多东西都是闻名而不解其意、知之却未得乎要。\n\n所以想静下心来，钻研理论并徒手实现，就算不是最优解答，至少有个朴素精确的领悟。\n\n## 目录\n\n### [线性回归](./线性回归.ipynb)\n### [逻辑回归](./逻辑回归.ipynb)\n### [感知机](./感知机.ipynb)\n### [K最近邻](./K最近邻.ipynb)\n### [K均值聚类](./K均值聚类.ipynb)\n### [一个隐含层的简单神经网络](./一个隐含层的简单神经网络.ipynb)\n### [多项逻辑回归](./多项逻辑回归.ipynb)\n\n---\n# 参考\n[机器学习基础][0]\n\n[逻辑回归成本函数及Sigmoid函数推导过程][1]\n\n[吴恩达斯坦福机器学习笔记][2]\n\n[向后传播][3]\n\n---\n[0]: https://github.com/zotroneneis/machine_learning_basics  \"美女研究生大作\"\n[1]: https://stats.stackexchange.com/questions/278771/how-is-the-cost-function-from-logistic-regression-derivated \"逻辑回归成本函数推导过程\"\n[2]: https://github.com/yoyoyohamapi/mit-ml \"公开在Coursera上的\"\n[3]: https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/ \"向后传播演算\"","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnagexiucai%2Fmla","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnagexiucai%2Fmla","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnagexiucai%2Fmla/lists"}