{"id":16686240,"url":"https://github.com/oldratlee/deep-learning-math","last_synced_at":"2026-02-25T10:04:03.087Z","repository":{"id":149710340,"uuid":"364664210","full_name":"oldratlee/deep-learning-math","owner":"oldratlee","description":"《深度学习的数学》的随书Excel文件","archived":false,"fork":false,"pushed_at":"2021-05-08T02:53:55.000Z","size":33627,"stargazers_count":27,"open_issues_count":0,"forks_count":12,"subscribers_count":3,"default_branch":"main","last_synced_at":"2025-03-06T10:03:01.538Z","etag":null,"topics":["backpropagation","deep-learning","deep-learning-introduction","excel","introduction","math"],"latest_commit_sha":null,"homepage":"https://github.com/oldratlee/deep-learning-math","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/oldratlee.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"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}},"created_at":"2021-05-05T18:01:29.000Z","updated_at":"2025-02-24T03:18:10.000Z","dependencies_parsed_at":"2024-02-22T16:49:18.557Z","dependency_job_id":null,"html_url":"https://github.com/oldratlee/deep-learning-math","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/oldratlee%2Fdeep-learning-math","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/oldratlee%2Fdeep-learning-math/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/oldratlee%2Fdeep-learning-math/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/oldratlee%2Fdeep-learning-math/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/oldratlee","download_url":"https://codeload.github.com/oldratlee/deep-learning-math/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243428436,"owners_count":20289317,"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":["backpropagation","deep-learning","deep-learning-introduction","excel","introduction","math"],"created_at":"2024-10-12T15:05:04.145Z","updated_at":"2025-10-26T04:49:55.250Z","avatar_url":"https://github.com/oldratlee.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# [《深度学习的数学》](https://book.douban.com/subject/33414479/)的随书`Excel`文件\n\n\u003cimg src=\"face.jpeg\" width=\"20%\" align=\"right\" /\u003e\n\n`Excel`文件列表：\n\n- [2-11_梯度下降法.xlsx](excels/2-11_梯度下降法.xlsx)\n- [3-5_NN（求解器）.xlsx](excels/3-5_NN（求解器）.xlsx)\n- [4-4_NN（误差反向传播法）.xlsx](excels/4-4_NN（误差反向传播法）.xlsx)\n- [5-4_CNN（求解器）.xlsx](excels/5-4_CNN（求解器）.xlsx)\n- [5-6_CNN（误差反向传播法）.xlsx](excels/5-6_CNN（误差反向传播法）.xlsx)\n- [附录A.xlsx](excels/附录A.xlsx)\n- [附录B.xlsx](excels/附录B.xlsx)\n\n## 个人的阅读感受\n\n日本老师深入浅出的讲解功力是较好的。能有感觉地了解之前看起来难懂的一些概念、算法思路，如\n\n- 回归（近似表示/考察关系）、回归分析、线性回归、回归方程/直线\n- 梯度、哈密顿算子（`Hamiltonian`、▽ 记号）\n- 误差反向传播法（简称反向传播，`Backpropagation`，缩写`BP`）  \n    `BP`一直是被应用得最广泛的机器学习算法之一，影响深远。\n\n肯定不能说自己理解了，只是有一些感觉了。当然也可能是因为之前相关的资料/书，自己没有耐心读下来～ 😁\n\n## 相关资料\n\n- 个人整理的相关主题书单：\n    - **深度学习** 书单：https://www.douban.com/doulist/119758049/\n    - **机器学习** 书单：https://www.douban.com/doulist/45886960/\n        - 基于`Python`动手实践的机器学习书单：https://www.douban.com/doulist/45885813/\n    - （探索性）**数据分析**（`EDA`） 书单：https://www.douban.com/doulist/45963852/  \n        - 这个书单包含了`Excel`主题的书。`Excel`应该是使用人数最多的数据分析工具。\n        - 数据科学这个词火起来之前，大家用词是数据分析，所以2者区别不明显，2个主题可以一起看。\n    - 更综合的 **数据科学** 书单：https://www.douban.com/doulist/119731263/\n- 随书`Excel`文件的下载来源地址：[《深度学习的数学》- 图灵社区](https://www.ituring.com.cn/book/2593)\n- 日版原版信息：[《Excelでわかるディープラーニング超入門: 涌井 良幸, 涌井 貞美》- Amazon.co.jp](https://www.amazon.co.jp/dp/4774194743)\n    - 书名直译是《用`Excel`就能明白的深度学习超入门》，原书名并没有『数学』 🤣\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Foldratlee%2Fdeep-learning-math","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Foldratlee%2Fdeep-learning-math","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Foldratlee%2Fdeep-learning-math/lists"}