斯坦福大学吴恩达机器学习课程(学习笔记和原始讲义)

上传者: l13890 | 上传时间: 2023-12-31 20:58:02 | 文件大小: 14.16MB | 文件类型: RAR
该课件为中科院一位仁兄在学习斯坦福大学吴恩达机器学习课程时候所做的学习笔记,非常好,吴老师上课略过的一些内容笔记都详细给出,并且还做了适当补充。强烈推荐。

文件下载

资源详情

[{"title":"( 38 个子文件 14.16MB ) 斯坦福大学吴恩达机器学习课程(学习笔记和原始讲义)","children":[{"title":"Stanford+Universtiy+Machine+Learning%28含学习笔记和原始讲义%29","children":[{"title":"斯坦福大学机器学习课程个人学习笔记(下)","children":[{"title":"(13)因子分析.pdf <span style='color:#111;'> 952.74KB </span>","children":null,"spread":false},{"title":"(14)增强学习.pdf <span style='color:#111;'> 899.98KB </span>","children":null,"spread":false},{"title":"(15)典型关联分析.pdf <span style='color:#111;'> 961.54KB </span>","children":null,"spread":false},{"title":"(10)主成分分析.pdf <span style='color:#111;'> 1.72MB </span>","children":null,"spread":false},{"title":"(16)偏最小二乘法回归.pdf <span style='color:#111;'> 279.08KB </span>","children":null,"spread":false},{"title":"请先查看该说明.txt <span style='color:#111;'> 910B </span>","children":null,"spread":false},{"title":"(12)线性判别分析.pdf <span style='color:#111;'> 918.07KB </span>","children":null,"spread":false},{"title":"(11)独立成分分析.pdf <span style='color:#111;'> 905.68KB </span>","children":null,"spread":false},{"title":"(9)在线学习.pdf <span style='color:#111;'> 530.77KB </span>","children":null,"spread":false}],"spread":true},{"title":"斯坦福大学机器学习课程原始讲义","children":[{"title":"cs229-notes7a.pdf <span style='color:#111;'> 264.67KB </span>","children":null,"spread":false},{"title":"cs229-notes9.pdf <span style='color:#111;'> 81.16KB </span>","children":null,"spread":false},{"title":"cs229-gp.pdf <span style='color:#111;'> 150.96KB </span>","children":null,"spread":false},{"title":"cs229-prob.pdf <span style='color:#111;'> 147.50KB </span>","children":null,"spread":false},{"title":"cs229-linalg.pdf <span style='color:#111;'> 164.59KB </span>","children":null,"spread":false},{"title":"cs229-cvxopt2.pdf <span style='color:#111;'> 196.80KB </span>","children":null,"spread":false},{"title":"cs229-notes5.pdf <span style='color:#111;'> 86.63KB </span>","children":null,"spread":false},{"title":"cs229-notes7b.pdf <span style='color:#111;'> 53.89KB </span>","children":null,"spread":false},{"title":"cs229-notes1.pdf <span style='color:#111;'> 229.65KB </span>","children":null,"spread":false},{"title":"ML-advice.pdf <span style='color:#111;'> 313.47KB </span>","children":null,"spread":false},{"title":"cs229-notes10.pdf <span style='color:#111;'> 75.40KB </span>","children":null,"spread":false},{"title":"cs229-notes12.pdf <span style='color:#111;'> 73.96KB </span>","children":null,"spread":false},{"title":"cs229-notes4.pdf <span style='color:#111;'> 108.74KB </span>","children":null,"spread":false},{"title":"cs229-notes3.pdf <span style='color:#111;'> 175.57KB </span>","children":null,"spread":false},{"title":"cs229-hmm.pdf <span style='color:#111;'> 197.80KB </span>","children":null,"spread":false},{"title":"cs229-notes8.pdf <span style='color:#111;'> 81.18KB </span>","children":null,"spread":false},{"title":"cs229-cvxopt.pdf <span style='color:#111;'> 148.86KB </span>","children":null,"spread":false},{"title":"cs229-notes11.pdf <span style='color:#111;'> 74.18KB </span>","children":null,"spread":false},{"title":"cs229-notes6.pdf <span style='color:#111;'> 50.85KB </span>","children":null,"spread":false},{"title":"cs229-notes2.pdf <span style='color:#111;'> 858.17KB </span>","children":null,"spread":false}],"spread":false},{"title":"斯坦福大学机器学习课程个人学习笔记(上)","children":[{"title":"(3)支持向量机SVM(上).pdf <span style='color:#111;'> 877.86KB </span>","children":null,"spread":false},{"title":"(2)判别模型、生成模型与朴素贝叶斯方法.pdf <span style='color:#111;'> 1.04MB </span>","children":null,"spread":false},{"title":"(7)混合高斯模型和EM算法.pdf <span style='color:#111;'> 436.95KB </span>","children":null,"spread":false},{"title":"请先查看该说明.txt <span style='color:#111;'> 910B </span>","children":null,"spread":false},{"title":"(1)线性回归、logistic回归和一般回归.pdf <span style='color:#111;'> 842.55KB </span>","children":null,"spread":false},{"title":"(8)EM算法.pdf <span style='color:#111;'> 757.24KB </span>","children":null,"spread":false},{"title":"(6)K-means聚类算法.pdf <span style='color:#111;'> 532.76KB </span>","children":null,"spread":false},{"title":"(5)规则化和模型选择.pdf <span style='color:#111;'> 895.02KB </span>","children":null,"spread":false},{"title":"(4)支持向量机SVM(下).pdf <span style='color:#111;'> 1.15MB </span>","children":null,"spread":false}],"spread":true}],"spread":true}],"spread":true}]

评论信息

免责申明

【只为小站】的资源来自网友分享,仅供学习研究,请务必在下载后24小时内给予删除,不得用于其他任何用途,否则后果自负。基于互联网的特殊性,【只为小站】 无法对用户传输的作品、信息、内容的权属或合法性、合规性、真实性、科学性、完整权、有效性等进行实质审查;无论 【只为小站】 经营者是否已进行审查,用户均应自行承担因其传输的作品、信息、内容而可能或已经产生的侵权或权属纠纷等法律责任。
本站所有资源不代表本站的观点或立场,基于网友分享,根据中国法律《信息网络传播权保护条例》第二十二条之规定,若资源存在侵权或相关问题请联系本站客服人员,zhiweidada#qq.com,请把#换成@,本站将给予最大的支持与配合,做到及时反馈和处理。关于更多版权及免责申明参见 版权及免责申明