{"id":18479305,"url":"https://github.com/mleveryday/homemade-machine-learning-cn","last_synced_at":"2025-04-08T15:34:30.943Z","repository":{"id":39492236,"uuid":"163261815","full_name":"MLEveryday/homemade-machine-learning-cn","owner":"MLEveryday","description":"自制机器学习","archived":false,"fork":false,"pushed_at":"2019-03-29T10:24:14.000Z","size":1640,"stargazers_count":72,"open_issues_count":1,"forks_count":35,"subscribers_count":16,"default_branch":"master","last_synced_at":"2025-03-23T16:11:14.319Z","etag":null,"topics":["algorithm","jupyter-notebook","machine-learning","python"],"latest_commit_sha":null,"homepage":"","language":"Python","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/MLEveryday.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}},"created_at":"2018-12-27T07:22:17.000Z","updated_at":"2025-03-13T01:34:38.000Z","dependencies_parsed_at":"2022-09-06T03:10:42.986Z","dependency_job_id":null,"html_url":"https://github.com/MLEveryday/homemade-machine-learning-cn","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/MLEveryday%2Fhomemade-machine-learning-cn","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MLEveryday%2Fhomemade-machine-learning-cn/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MLEveryday%2Fhomemade-machine-learning-cn/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MLEveryday%2Fhomemade-machine-learning-cn/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/MLEveryday","download_url":"https://codeload.github.com/MLEveryday/homemade-machine-learning-cn/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247872139,"owners_count":21010183,"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":["algorithm","jupyter-notebook","machine-learning","python"],"created_at":"2024-11-06T12:14:04.405Z","updated_at":"2025-04-08T15:34:27.617Z","avatar_url":"https://github.com/MLEveryday.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 自制机器学习\n[homemade-machine-learning](https://github.com/trekhleb/homemade-machine-learning)\n\n\u003e 本项目包含了用 **Python** 实现流行的机器学习算法例子和他们背后的数学原理说明。每个例子包含交互的 **Jupyter Notebook** demo，你可以操作训练数据，算法配置，并且可以在你的播放器内立即看到结果、图表和预测值。本项目的大部分例子都基于吴恩达的《[机器学习课程](https://www.coursera.org/learn/machine-learning)》。\n\n本项目的目的不在于使用第三方库仅用几行完成机器学习算法，而是从零开始，使用算法背后的数学进行编写。这也是为什么所有算法实现都被称为“自制”，而不是为了产品应用。\n\n## 有监督学习 Supervised Learning\n\n在有监督学习中，我们有一个训练数据集合作为输入，每个训练集合有一个标签或“真值”集合作为输出。然后，我们训练模型（机器学习算法参数）正确地映射（正确预测）输入和输出。最终目的是找到一组模型参数，可以成功的把新数据的输入映射（预测）到输出。\n\n### 回归 Regression\n\n我们在回归问题中进行真实值预测。一般我们试图在训练数据中绘制线/平面/n维平面。\n\n用途举例：股价预测，销售分析，任意数字的依赖度等。\n\n#### 🤖 线性回归 Linear Regression\n\n\n\n### 分类器 Classification\n\n#### 🤖 逻辑回归 Logistic Regression\n\n\n\n## 无监督学习 Unsupervised Learning\n\n### 聚类 Clustering\n\n#### 🤖 k-均值算法 K-means Algorithm\n\n\n\n### 异常检测 Anomaly Detection\n\n异常检测（也叫outliner deteciton）是识别出来与大多数数据有显著不同的样本，事件或观察样例。\n\n用途举例：入侵检测，欺诈检测，系统良性检测，从数据集中取出异常数据等。\n\n#### 🤖 应用高斯分布的异常检测 Anomaly Detection using Gaussian Distribution\n\n*[[数学 | 应用高斯分布的异常检测](/homemade/anomaly_detection/README.md)]-理论和扩展阅读链接  \n\n*[[代码 | 应用高斯分布的异常检测](/homemade/anomaly_detection/multilayer_perceptron.py)]-代码实例\n\n*[[演示 | 异常检测](https://nbviewer.jupyter.org/github/trekhleb/homemade-machine-learning/blob/master/notebooks/anomaly_detection/anomaly_detection_gaussian_demo.ipynb)]-查找服务器操作参数（如延迟```latency```和阈值```threshold```）中的异常\n\n### 神经网络 Neural Network (NN)\n\n神经网络本身不是一个算法，而是一个框架，很多不同机器学习算法一起工作，处理复杂的输入。\n\n用途举例：所有其他算法的替代算法，图像识别，语音识别，图像处理（应用特殊样式），语言翻译等。\n\n#### 🤖 多层感知 Multilayer Perceptron (MLP)\n\n* [[数学 | 多层感知器](/homemade/neural_network/README.md)] - 理论和扩展阅读链接\n\n\n\n## 机器学习路线图\n\n\n\n## 前提\n\n#### 安装 Python\n\n#### 安装依赖库\n\n#### 启动本地 Jupyter\n\n#### 启动远程 Jupyter\n\n\n\n## 数据\n\n\n\n## 贡献列表\n\n| 章节                  | 译者 |\n| --------------------- | ---- |\n| Linear Regression     |      |\n| Logistic Regression   |      |\n| Anomaly Detection     |      |\n| Anomaly Detection     |      |\n| Multilayer Perceptron |      |\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmleveryday%2Fhomemade-machine-learning-cn","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmleveryday%2Fhomemade-machine-learning-cn","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmleveryday%2Fhomemade-machine-learning-cn/lists"}