2021-ZJU-Machine-Learning:浙江大学机器学习-源码

2021-ZJU-机器学习浙江大学机器学习课程概述： 2016年，人工智能机器人AlphaGO击败了围棋世界冠军李世石，这场史无前例的“人机大战”将AI引入社会舆论的风口浪尖上。AI是什么？AI对人类有什么作用？扮演怎样的角色？要想弄清楚这些问题，就必须了解AI的主要的工作原理-机器学习。这门课程对机器学习这一领域既有全面细致的理论讲解，又有趣味生动的故事串联，还有妙趣横生的程序实践，引导同学们从数学，计算机科学和人文哲学等方面全面地理解AI。课程中的知识点覆盖全面，尤其是包含其中这一领域的发展，深度学习，增强学习，生成对抗网络，RNN和LSTM，ALPHAGO程序解析等最新的研究内容登上了MOOC的课堂。了十个左右的编程实验，并讲解流行的CAFFE，TENSORFLOW和PYTORCH等流行的深度学习框架，让大家在学习理论的同时亲身实践，体验AI的魅力。与国外Andrew N

文件下载

资源详情

[{"title":"（ 31 个子文件 168.87MB ） 2021-ZJU-Machine-Learning:浙江大学机器学习-源码","children":[{"title":"2021-ZJU-Machine-Learning-main","children":[{"title":"{5}--第五章强化学习","children":[{"title":"{4}--4.强化学习(AlphaGo上）","children":[{"title":"(5.4.1)--AlphaGo介绍.pdf <span style='color:#111;'> 1.84MB </span>","children":null,"spread":false}],"spread":true}],"spread":true},{"title":"{2}--第二章支持向量机","children":[{"title":"{12}--12.支持向量机（兵王问题MATLAB程序）","children":[{"title":"(2.12.1)--1718SVM.zip <span style='color:#111;'> 1.72MB </span>","children":null,"spread":false}],"spread":true},{"title":"{1}--1.支持向量机（线性可分定义）","children":[{"title":"(2.1.1)--向量偏导的定义.pdf <span style='color:#111;'> 206.75KB </span>","children":null,"spread":false}],"spread":true},{"title":"{7}--7.支持向量机（原问题和对偶问题）","children":[{"title":"(2.7.1)--强对偶定理证明.pdf <span style='color:#111;'> 2.87MB </span>","children":null,"spread":false}],"spread":true},{"title":"{9}--9.支持向量机（算法流程）","children":[{"title":"(2.9.2)--支持向量机的应用.pdf <span style='color:#111;'> 836.51KB </span>","children":null,"spread":false},{"title":"(2.9.1)--支持向量机的理论推导.pdf <span style='color:#111;'> 3.11MB </span>","children":null,"spread":false}],"spread":true}],"spread":true},{"title":"{3}--第三章人工神经网络","children":[{"title":"{1}--1.人工神经网络（章节总览）","children":[{"title":"(3.1.1)--人工神经网络介绍.pdf <span style='color:#111;'> 2.03MB </span>","children":null,"spread":false},{"title":"(3.1.2)--本章资源介绍.pdf <span style='color:#111;'> 36.99KB </span>","children":null,"spread":false}],"spread":true},{"title":"{7}--7.人工神经网络（后向传播算法上）","children":[{"title":"(3.7.1)--后向传播算法推导.pdf <span style='color:#111;'> 1.02MB </span>","children":null,"spread":false}],"spread":true},{"title":"{2}--2.人工神经网络（感知器算法）","children":[{"title":"(3.2.1)--感知器算法证明.pdf <span style='color:#111;'> 57.53KB </span>","children":null,"spread":false},{"title":"(3.2.2)--第二十二讲生成图片的程序.zip <span style='color:#111;'> 7.08MB </span>","children":null,"spread":false}],"spread":true},{"title":"{10}--10.人工神经网络（兵王问题MATLAB程序）","children":[{"title":"(3.10.2)--NeuralNetworks.rar <span style='color:#111;'> 2.25MB </span>","children":null,"spread":false},{"title":"(3.10.1)--NNexamples.rar <span style='color:#111;'> 35.74MB </span>","children":null,"spread":false}],"spread":true},{"title":"{11}--11.人工神经网络（参数设置）","children":[{"title":"(3.11.1)--参数设置.pdf <span style='color:#111;'> 1.41MB </span>","children":null,"spread":false}],"spread":true}],"spread":true},{"title":"{6}--第六章传统机器学习","children":[{"title":"{3}--1.主成分分析","children":[{"title":"(6.3.3)--概率分类法.pdf <span style='color:#111;'> 1.11MB </span>","children":null,"spread":false},{"title":"(6.3.1)--子空间算法.pdf <span style='color:#111;'> 963.38KB </span>","children":null,"spread":false},{"title":"(6.3.2)--特征选择与提升算法.pdf <span style='color:#111;'> 1.21MB </span>","children":null,"spread":false}],"spread":true},{"title":"{4}--2.K-均值聚类","children":[{"title":"(6.4.1)--基于k-means算法的图像矢量量化（课件）.pdf <span style='color:#111;'> 971.56KB </span>","children":null,"spread":false},{"title":"(6.4.2)--kmeans_图像矢量量化.rar <span style='color:#111;'> 466.33KB </span>","children":null,"spread":false}],"spread":true},{"title":"{5}--3.高斯混合模型","children":[{"title":"(6.5.3)--语音识别介绍.pdf <span style='color:#111;'> 1.21MB </span>","children":null,"spread":false},{"title":"(6.5.5)--隐含马尔可夫过程（2）.pdf <span style='color:#111;'> 970.58KB </span>","children":null,"spread":false},{"title":"(6.5.4)--隐含马尔可夫过程（1）.pdf <span style='color:#111;'> 269.70KB </span>","children":null,"spread":false},{"title":"(6.5.2)--GMM在说话人识别中的应用.pdf <span style='color:#111;'> 944.06KB </span>","children":null,"spread":false},{"title":"(6.5.1)--EM算法.pdf <span style='color:#111;'> 1.20MB </span>","children":null,"spread":false}],"spread":true}],"spread":true},{"title":"README.md <span style='color:#111;'> 2.26KB </span>","children":null,"spread":false},{"title":"{4}--第四章深度学习","children":[{"title":"{11}--9.目标检测与分割下","children":[{"title":"(4.11.2)--faceDetectionMTCNNSingleImage.ra <span style='color:#111;'> 7.69MB </span>","children":null,"spread":false},{"title":"(4.11.1)--faceDetectionAdaboost.rar <span style='color:#111;'> 2.39MB </span>","children":null,"spread":false},{"title":"(4.11.3)--faceRecognitionCASIA.rar <span style='color:#111;'> 45.75MB </span>","children":null,"spread":false}],"spread":true},{"title":"{5}--3.深度学习（卷积神经网络LENET）","children":[{"title":"(4.5.2)--nnMnist_tensorflow(第三十六讲程序).zip <span style='color:#111;'> 11.07MB </span>","children":null,"spread":false},{"title":"(4.5.1)--testPytorchLenet.rar <span style='color:#111;'> 33.08MB </span>","children":null,"spread":false}],"spread":true},{"title":"{12}--10.时间序列的深度学习模型（RNN和LSTM）","children":[{"title":"(4.12.1)--RNN和LSTM课件.pdf <span style='color:#111;'> 1.94MB </span>","children":null,"spread":false}],"spread":true}],"spread":true}],"spread":true}],"spread":true}]

评论信息

其他资源

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