【尊享】ZX070 – 高阶机器学习5期 [43.4G]

【尊享】ZX070 – 高阶机器学习5期 [43.4G]

┣━━00.视频 [43G]
┃ ┣━━00.第00章-开班典礼 [590.5M]
┃ ┃ ┣━━开班典礼-1.vep [169.5M]
┃ ┃ ┣━━开班典礼-2.vep [231.2M]
┃ ┃ ┗━━开班典礼-3.vep [189.8M]
┃ ┣━━01.第01章-凸优化介绍 [2.9G]
┃ ┃ ┣━━算法复杂度回顾：p和np-1.vep [262.7M]
┃ ┃ ┣━━算法复杂度回顾：p和np-2.vep [347.8M]
┃ ┃ ┣━━凸优化介绍-1.vep [317.8M]
┃ ┃ ┣━━凸优化介绍-2.vep [151.6M]
┃ ┃ ┣━━凸优化介绍-3.vep [437.2M]
┃ ┃ ┣━━凸优化介绍-4.vep [331.6M]
┃ ┃ ┣━━凸优化介绍-5.vep [352.8M]
┃ ┃ ┣━━线性规划：实战案例-1.vep [281.3M]
┃ ┃ ┗━━线性规划：实战案例-2.vep [449.3M]
┃ ┣━━02.第02章-判定凸函数 [2.4G]
┃ ┃ ┣━━论文解读：wmd by killian-1.vep [549.5M]
┃ ┃ ┣━━论文解读：wmd by killian-2.vep [322.6M]
┃ ┃ ┣━━判定凸函数-1.vep [320.1M]
┃ ┃ ┣━━判定凸函数-2.vep [237.2M]
┃ ┃ ┣━━判定凸函数-3.vep [238.7M]
┃ ┃ ┣━━判定凸函数-4.vep [248.8M]
┃ ┃ ┣━━线性回归模型正则化elastic net和group lasso-1.vep [315.2M]
┃ ┃ ┗━━线性回归模型正则化elastic net和group lasso-2.vep [270.8M]
┃ ┣━━03.第03章-凸优化问题 [2G]
┃ ┃ ┣━━半正定规划-1.vep [240M]
┃ ┃ ┣━━半正定规划-2.vep [313.8M]
┃ ┃ ┣━━凸优化问题-1.vep [195.9M]
┃ ┃ ┣━━凸优化问题-2.vep [231.8M]
┃ ┃ ┣━━凸优化问题-3.vep [222.3M]
┃ ┃ ┣━━凸优化问题-4.vep [226.8M]
┃ ┃ ┣━━整数规划案例解析-1.vep [355.4M]
┃ ┃ ┗━━整数规划案例解析-2.vep [259M]
┃ ┣━━04.第04章-对偶（Duality） [3.9G]
┃ ┃ ┣━━对偶（duality）-1.vep [272.8M]
┃ ┃ ┣━━对偶（duality）-2.vep [211.2M]
┃ ┃ ┣━━对偶（duality）-3.vep [231.4M]
┃ ┃ ┣━━损失函数的比较(loss_functions)-1.vep [437.8M]
┃ ┃ ┣━━损失函数的比较(loss_functions)-2.vep [343.2M]
┃ ┃ ┣━━优化与量化投资（项目解析）-1.vep [437.8M]
┃ ┃ ┣━━优化与量化投资（项目解析）-2.vep [287.5M]
┃ ┃ ┣━━优化与量化投资（项目解析）-3.vep [705.9M]
┃ ┃ ┣━━优化与量化投资（项目解析）-4.vep [166.5M]
┃ ┃ ┣━━优化与量化投资（项目解析）-5.vep [634.9M]
┃ ┃ ┣━━svm的primal和dual-1.vep [191.3M]
┃ ┃ ┗━━svm的primal和dual-2.vep [119.7M]
┃ ┣━━05.第05章-优化技术 [2.2G]
┃ ┃ ┣━━优化技术-1.vep [203.2M]
┃ ┃ ┣━━优化技术-2.vep [233.6M]
┃ ┃ ┣━━优化技术-3.vep [394.2M]
┃ ┃ ┣━━admm-1.vep [297.4M]
┃ ┃ ┣━━admm-2.vep [524.8M]
┃ ┃ ┣━━stochastic optimization-1.vep [518.2M]
┃ ┃ ┗━━stochastic optimization-2.vep [98M]
┃ ┣━━06.第06章-数学基础 [1.9G]
┃ ┃ ┣━━矩阵分解方法介绍-1.vep [242.5M]
┃ ┃ ┣━━矩阵分解方法介绍-2.vep [467.1M]
┃ ┃ ┣━━数学基础-1.vep [292.4M]
┃ ┃ ┣━━数学基础-2.vep [268.4M]
┃ ┃ ┣━━数学基础-3.vep [261.4M]
┃ ┃ ┗━━cnn的卷积和池化.vep [372.3M]
┃ ┣━━07.第07章-谱域（Spectral Domain）的图神经网络 [2.4G]
┃ ┃ ┣━━谱域（spectral domain）的图神经网络-1.vep [344.4M]
┃ ┃ ┣━━谱域（spectral domain）的图神经网络-2.vep [292.4M]
┃ ┃ ┣━━cnn weight prunning cnn的权重剪枝-1.vep [249.9M]
┃ ┃ ┣━━cnn weight prunning cnn的权重剪枝-2.vep [562.7M]
┃ ┃ ┣━━gcn代码解读-1.vep [556.6M]
┃ ┃ ┗━━gcn代码解读-2.vep [488.7M]
┃ ┣━━08.第08章-Attention 机制，GAT，EGCN, Monet [2.5G]
┃ ┃ ┣━━attention 机制，gat，egcn, monet-1.vep [130M]
┃ ┃ ┣━━attention 机制，gat，egcn, monet-2.vep [339.5M]
┃ ┃ ┣━━attention 机制，gat，egcn, monet-3.vep [386M]
┃ ┃ ┣━━attention机制在nlp的应用-1.vep [252.3M]
┃ ┃ ┣━━attention机制在nlp的应用-2.vep [197.1M]
┃ ┃ ┣━━gat代码讲解-1.vep [630.5M]
┃ ┃ ┗━━gat代码讲解-2.vep [608.9M]
┃ ┣━━09.第09章-图神经网络改进与应用图神经网络改进与应用 [1.9G]
┃ ┃ ┣━━图神经网络改进与应用图神经网络改进与应用-1.vep [180.2M]
┃ ┃ ┣━━图神经网络改进与应用图神经网络改进与应用-2.vep [195.5M]
┃ ┃ ┣━━图神经网络改进与应用图神经网络改进与应用-3.vep [106.4M]
┃ ┃ ┣━━图神经网络改进与应用图神经网络改进与应用-4.vep [110.6M]
┃ ┃ ┣━━graphsage 论文讲解+代码实践-1.vep [189.3M]
┃ ┃ ┣━━graphsage 论文讲解+代码实践-2.vep [565.4M]
┃ ┃ ┣━━hypergcn 论文讲解+代码实践-1.vep [220.5M]
┃ ┃ ┗━━hypergcn 论文讲解+代码实践-2.vep [425.4M]
┃ ┣━━10.第10章-强化学习基础 [2.9G]
┃ ┃ ┣━━强化学习基础_1-1.vep [278.8M]
┃ ┃ ┣━━强化学习基础_1-2.vep [495.8M]
┃ ┃ ┣━━强化学习基础_1-3.vep [564.2M]
┃ ┃ ┣━━强化学习基础_1-4.vep [356.2M]
┃ ┃ ┣━━强化学习基础_2.vep [478M]
┃ ┃ ┣━━强化学习基础_4.vep [454M]
┃ ┃ ┗━━项目布置与作业一布置.vep [306.4M]
┃ ┣━━11.第11章-Playing Atari with Deep Reinforcement Learning [996.2M]
┃ ┃ ┣━━playing atari with deep reinforcement learning-1.vep [427.3M]
┃ ┃ ┗━━playing atari with deep reinforcement learning-2.vep [568.9M]
┃ ┣━━12.第12章-强化学习基础 [1.8G]
┃ ┃ ┣━━强化学习基础_3-1.vep [411.1M]
┃ ┃ ┣━━强化学习基础_3-2.vep [340.3M]
┃ ┃ ┣━━强化学习基础_3-3.vep [105M]
┃ ┃ ┣━━强化学习基础_3-4.vep [231.7M]
┃ ┃ ┣━━强化学习基础_4.vep [417.9M]
┃ ┃ ┗━━作业讲解与布置.vep [316.6M]
┃ ┣━━13.第13章-自然语言处理中的RL [2.6G]
┃ ┃ ┣━━强化学习于金融场景应用.vep [326.2M]
┃ ┃ ┣━━文本生成 + 强化学习 实战-1.vep [356.5M]
┃ ┃ ┣━━文本生成 + 强化学习 实战-2.vep [309.5M]
┃ ┃ ┣━━自然语言处理中的rl-1.vep [328.3M]
┃ ┃ ┣━━自然语言处理中的rl-2.vep [273.8M]
┃ ┃ ┣━━自然语言处理中的rl-3.vep [311.1M]
┃ ┃ ┣━━自然语言处理中的rl-4.vep [220.6M]
┃ ┃ ┗━━作业三讲解.vep [558.5M]
┃ ┣━━14.第14章-Bandits [2.4G]
┃ ┃ ┣━━20210807 ml5 workshop 作业讲解.vep [447.6M]
┃ ┃ ┣━━bandits-1.vep [147.4M]
┃ ┃ ┣━━bandits-2.vep [172.2M]
┃ ┃ ┣━━bandits-3.vep [249.6M]
┃ ┃ ┣━━bandits-4.vep [186M]
┃ ┃ ┣━━bandits-5.vep [242.7M]
┃ ┃ ┣━━master the game of go without human knowledge-1.vep [264.3M]
┃ ┃ ┗━━master the game of go without human knowledge-2.vep [726.9M]
┃ ┣━━15.第15章-贝叶斯方法论简介 [1.5G]
┃ ┃ ┣━━贝叶斯方法论简介-1.vep [326.7M]
┃ ┃ ┣━━贝叶斯方法论简介-2.vep [196M]
┃ ┃ ┣━━贝叶斯方法论简介-3.vep [347.3M]
┃ ┃ ┣━━linear regression(mle), ridge regression(map), bayesian linear regression (bayesian)-1.vep [341.6M]
┃ ┃ ┗━━linear regression(mle), ridge regression(map), bayesian linear regression (bayesian)-2.vep [354.5M]
┃ ┣━━16.第16章-主题模型 [2.9G]
┃ ┃ ┣━━主题模型-1.vep [297.9M]
┃ ┃ ┣━━主题模型-2.vep [197.1M]
┃ ┃ ┣━━主题模型-3.vep [366.5M]
┃ ┃ ┣━━bayesian neural network introduction-3.vep [242.1M]
┃ ┃ ┣━━bayesian neural network introduction-4.vep [260.3M]
┃ ┃ ┣━━topic model as a block-box-1.vep [701.5M]
┃ ┃ ┗━━topic model as a block-box-2.vep [864M]
┃ ┣━━17.第17章-Edward library for Bayesian Learning [2.2G]
┃ ┃ ┣━━edward library for bayesian learning.vep [685.1M]
┃ ┃ ┣━━mcmc方法-1.vep [145.7M]
┃ ┃ ┣━━mcmc方法-2.vep [213.9M]
┃ ┃ ┣━━mcmc方法-3.vep [248.8M]
┃ ┃ ┣━━variational autoencoder（vae）-1.vep [418.1M]
┃ ┃ ┗━━variational autoencoder（vae）-2.vep [497.2M]
┃ ┣━━18.第18章-变分法（Variational Method) [1.4G]
┃ ┃ ┣━━变分法（variational method)-1.vep [432.4M]
┃ ┃ ┣━━变分法（variational method)-2.vep [231.5M]
┃ ┃ ┣━━paper阅读：rethinking lda-1.vep [391.1M]
┃ ┃ ┗━━paper阅读：rethinking lda-2.vep [352.3M]
┃ ┣━━19.第19章-贝叶斯其他前沿主题 [1G]
┃ ┃ ┣━━贝叶斯其他前沿主题-1.vep [290.9M]
┃ ┃ ┗━━贝叶斯其他前沿主题-2.vep [768.3M]
┃ ┗━━20.第20章-就业指导 [712.2M]
┃ ┣━━01.就业指导.vep [457.1M]
┃ ┗━━02.就业指导.vep [255M]
┗━━01.资料.zip [397.6M]

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