【尊享】ZX039 – 机器学习高阶训练营4期 [57.9G]

【尊享】ZX039 – 机器学习高阶训练营4期 [57.9G]

┣━━01.视频 [57.5G]
┃ ┣━━0.开班典礼 [535.2M]
┃ ┃ ┣━━开班典礼1杨栋老师的分享_.vep [59.2M]
┃ ┃ ┣━━开班典礼2王老师分享JerryWang_.vep [27.6M]
┃ ┃ ┣━━开班典礼3文哲老师课程介绍_.vep [49.5M]
┃ ┃ ┣━━开班典礼4课程安排算法复杂度相关_.vep [78.6M]
┃ ┃ ┣━━开班典礼5最大似然估计最大后验估计_.vep [49.1M]
┃ ┃ ┣━━开班典礼6梯度下降法_.vep [57.4M]
┃ ┃ ┣━━开班典礼7_.vep [66.8M]
┃ ┃ ┗━━开班典礼8疑难解答_.vep [147M]
┃ ┣━━1.第一周凸优化 [4.5G]
┃ ┃ ┣━━算法复杂度回顾P和NP1_.vep [376.6M]
┃ ┃ ┣━━算法复杂度回顾P和NP2_.vep [555.3M]
┃ ┃ ┣━━凸优化介绍1_.vep [447.3M]
┃ ┃ ┣━━凸优化介绍2_.vep [581.5M]
┃ ┃ ┣━━凸优化介绍3_.vep [543.8M]
┃ ┃ ┣━━凸优化介绍4_.vep [501.1M]
┃ ┃ ┣━━凸优化介绍5_.vep [381.6M]
┃ ┃ ┣━━凸优化介绍6_.vep [443.8M]
┃ ┃ ┣━━线性规划实战案例1_.vep [335.3M]
┃ ┃ ┗━━线性规划实战案例2_.vep [400.5M]
┃ ┣━━2.第二周凸函数 [4.3G]
┃ ┃ ┣━━论文解读WMDbyKillian2_.vep [435.2M]
┃ ┃ ┣━━判定凸函数1_.vep [396.3M]
┃ ┃ ┣━━判定凸函数2_.vep [303M]
┃ ┃ ┣━━判定凸函数3_.vep [301.9M]
┃ ┃ ┣━━判定凸函数4_.vep [234.5M]
┃ ┃ ┣━━判定凸函数5_.vep [431.6M]
┃ ┃ ┣━━判定凸函数6_.vep [366.7M]
┃ ┃ ┣━━凸函数判定与矩阵求导1_.vep [284.5M]
┃ ┃ ┣━━凸函数判定与矩阵求导2_.vep [330.6M]
┃ ┃ ┣━━凸函数判定与矩阵求导3_.vep [197.1M]
┃ ┃ ┣━━线性回归模型正则化ElasticNet和GroupLasso1_.vep [451.5M]
┃ ┃ ┣━━线性回归模型正则化ElasticNet和GroupLasso2_.vep [302.8M]
┃ ┃ ┗━━线性回归模型正则化ElasticNet和GroupLasso3_.vep [354.6M]
┃ ┣━━3.第三周凸优化问题 [4.5G]
┃ ┃ ┣━━半正定规划1_.vep [614M]
┃ ┃ ┣━━半正定规划2_.vep [508.6M]
┃ ┃ ┣━━凸优化问题1_.vep [354.1M]
┃ ┃ ┣━━凸优化问题2_.vep [525.2M]
┃ ┃ ┣━━凸优化问题3_.vep [418.8M]
┃ ┃ ┣━━凸优化问题4_.vep [379.9M]
┃ ┃ ┣━━凸优化问题5_.vep [352.2M]
┃ ┃ ┣━━凸优化问题6_.vep [304.2M]
┃ ┃ ┣━━凸优化问题7_.vep [436.6M]
┃ ┃ ┣━━整数规划案例解析1_.vep [374.8M]
┃ ┃ ┗━━整数规划案例解析2_.vep [333.6M]
┃ ┣━━4.第四周优化与量化投资 [3.7G]
┃ ┃ ┣━━优化与量化投资1_.vep [498.7M]
┃ ┃ ┣━━优化与量化投资2_.vep [441.6M]
┃ ┃ ┣━━优化与量化投资3_.vep [384.9M]
┃ ┃ ┣━━优化与量化投资4_.vep [285.5M]
┃ ┃ ┣━━优化与量化投资5_.vep [432.5M]
┃ ┃ ┣━━LMNN1_.vep [569.6M]
┃ ┃ ┣━━LMNN2_.vep [241.3M]
┃ ┃ ┣━━Nonconvexoptimization问题的python实战1_.vep [491.9M]
┃ ┃ ┗━━Nonconvexoptimization问题的python实战2_.vep [391.8M]
┃ ┣━━5.第五周对偶 [3.8G]
┃ ┃ ┣━━对偶Duality1_.vep [408.7M]
┃ ┃ ┣━━对偶Duality2_.vep [567.9M]
┃ ┃ ┣━━对偶Duality3_.vep [568.8M]
┃ ┃ ┣━━对偶Duality4_.vep [473.7M]
┃ ┃ ┣━━对偶Duality5_.vep [520.9M]
┃ ┃ ┣━━损失函数的比较1_.vep [417.8M]
┃ ┃ ┣━━SVM的对偶1_.vep [504.4M]
┃ ┃ ┗━━SVM的对偶2_.vep [408.4M]
┃ ┣━━6.第六周ADMM [1.2G]
┃ ┃ ┣━━ADMM1.vep [391.1M]
┃ ┃ ┣━━ADMM2.vep [484.2M]
┃ ┃ ┗━━ADMM3.vep [394M]
┃ ┣━━7.第七周数学基础 [2.9G]
┃ ┃ ┣━━矩阵分解方法介绍1.vep [282M]
┃ ┃ ┣━━矩阵分解方法介绍2.vep [516.3M]
┃ ┃ ┣━━数学基础1.vep [283.3M]
┃ ┃ ┣━━数学基础2.vep [195M]
┃ ┃ ┣━━数学基础3.vep [367.5M]
┃ ┃ ┣━━数学基础4.vep [445.1M]
┃ ┃ ┣━━数学基础5.vep [174.9M]
┃ ┃ ┣━━CNN的卷积和池化1.vep [344.2M]
┃ ┃ ┗━━CNN的卷积和池化2.vep [335.6M]
┃ ┣━━8.第八周谱域 [3G]
┃ ┃ ┣━━谱域SpectralDomain的图神经网络1.vep [271.2M]
┃ ┃ ┣━━谱域SpectralDomain的图神经网络2.vep [248.9M]
┃ ┃ ┣━━谱域SpectralDomain的图神经网络3.vep [268.6M]
┃ ┃ ┣━━谱域SpectralDomain的图神经网络4.vep [205.2M]
┃ ┃ ┣━━CNNweightprunningCNN的权重剪枝1.vep [395.5M]
┃ ┃ ┣━━CNNweightprunningCNN的权重剪枝2.vep [615.2M]
┃ ┃ ┣━━GCN代码解读1.vep [617.1M]
┃ ┃ ┗━━GCN代码解读2.vep [477.7M]
┃ ┣━━9.第九周Attention机制 [1.4G]
┃ ┃ ┣━━attention机制GATEGCN和Monet1.vep [385.2M]
┃ ┃ ┣━━attention机制GATEGCN和Monet2.vep [447.6M]
┃ ┃ ┣━━attention机制GATEGCN和Monet3.vep [389.2M]
┃ ┃ ┗━━attention机制GATEGCN和Monet4.vep [242.5M]
┃ ┣━━10.第十周图神经网络改进与应用图神经网络改进与应用 [3.1G]
┃ ┃ ┣━━Lecture 图神经网络改进与应用图神经网络改进与应用 [1.6G]
┃ ┃ ┃ ┣━━图神经网络改进与应用图神经网络改进与应用1.vep [213.6M]
┃ ┃ ┃ ┣━━图神经网络改进与应用图神经网络改进与应用2.vep [457.7M]
┃ ┃ ┃ ┣━━图神经网络改进与应用图神经网络改进与应用3.vep [247.6M]
┃ ┃ ┃ ┣━━图神经网络改进与应用图神经网络改进与应用4.vep [360.8M]
┃ ┃ ┃ ┗━━图神经网络改进与应用图神经网络改进与应用5.vep [390.1M]
┃ ┃ ┣━━Workshop Graphsage 论文讲解+代码实践 [784.5M]
┃ ┃ ┃ ┣━━Graphsage论文讲解代码实践1.vep [464.4M]
┃ ┃ ┃ ┗━━Graphsage论文讲解代码实践2.vep [320.1M]
┃ ┃ ┗━━Workshop HyperGCN 论文讲解+代码实践 [721.5M]
┃ ┃ ┣━━HyperGCN 论文讲解+代码实践-1.vep [260.6M]
┃ ┃ ┗━━HyperGCN 论文讲解+代码实践-2.vep [461M]
┃ ┣━━第11周 贝叶斯方法论简介 [2.4G]
┃ ┃ ┣━━Lecture贝叶斯方法论简介 [1.7G]
┃ ┃ ┃ ┣━━贝叶斯方法论简介-1.vep [524.9M]
┃ ┃ ┃ ┣━━贝叶斯方法论简介-2.vep [343.3M]
┃ ┃ ┃ ┣━━贝叶斯方法论简介-3.vep [466M]
┃ ┃ ┃ ┗━━贝叶斯方法论简介-4.vep [371.2M]
┃ ┃ ┗━━WorkshopLinear Regression(MLE), Ridge Regression(MAP), Bayesian Linear Regression (Bayesian) [787.2M]
┃ ┃ ┣━━Linear Regression(MLE), Ridge Regression(MAP), Bayesian Linear Regression (Bayesian)-1.vep [366.6M]
┃ ┃ ┗━━Linear Regression(MLE), Ridge Regression(MAP), Bayesian Linear Regression (Bayesian)-2.vep [420.6M]
┃ ┣━━第12周 主题模型 [3.3G]
┃ ┃ ┣━━Lecture主题模型 + 第一次项目讲解 [1.8G]
┃ ┃ ┃ ┣━━主题模型 + 第一次项目讲解-1.vep [460.3M]
┃ ┃ ┃ ┣━━主题模型 + 第一次项目讲解-2.vep [246M]
┃ ┃ ┃ ┣━━主题模型 + 第一次项目讲解-3.vep [486.4M]
┃ ┃ ┃ ┗━━主题模型 + 第一次项目讲解-4.vep [625.9M]
┃ ┃ ┣━━ReviewBayesian Neural Network Introduction [769.3M]
┃ ┃ ┃ ┣━━Bayesian Neural Network Introduction-1.vep [323.2M]
┃ ┃ ┃ ┗━━Bayesian Neural Network Introduction-2.vep [446M]
┃ ┃ ┗━━ReviewTopic Model as a block-box [749M]
┃ ┃ ┣━━Topic Model as a block-box-1.vep [421.2M]
┃ ┃ ┗━━Topic Model as a block-box-2.vep [327.8M]
┃ ┣━━第13周 MCMC方法 [2.8G]
┃ ┃ ┣━━Lecture MCMC方法 [1.7G]
┃ ┃ ┃ ┣━━MCMC方法-1.vep [259.2M]
┃ ┃ ┃ ┣━━MCMC方法-2.vep [505.4M]
┃ ┃ ┃ ┣━━MCMC方法-3.vep [376.9M]
┃ ┃ ┃ ┗━━MCMC方法-4.vep [549.1M]
┃ ┃ ┣━━Review Edward library for Bayesian Learning [471.1M]
┃ ┃ ┃ ┗━━Edward library for Bayesian Learning.vep [471.1M]
┃ ┃ ┗━━Review Variational Autoencoder（VAE） [688.9M]
┃ ┃ ┣━━Variational Autoencoder（VAE）-1.vep [396.5M]
┃ ┃ ┗━━Variational Autoencoder（VAE）-2.vep [292.3M]
┃ ┣━━第14周 变分法（Variational Method) [1G]
┃ ┃ ┣━━变分法（Variational Method)-1.vep [425.2M]
┃ ┃ ┗━━变分法（Variational Method)-2.vep [613M]
┃ ┣━━第15周 强化学习基础-上 [3.7G]
┃ ┃ ┣━━Lecture 强化学习基础 1 [2G]
┃ ┃ ┃ ┣━━强化学习基础_1-1.vep [389M]
┃ ┃ ┃ ┣━━强化学习基础_1-2.vep [466.7M]
┃ ┃ ┃ ┣━━强化学习基础_1-3.vep [502.5M]
┃ ┃ ┃ ┣━━强化学习基础_1-4.vep [399.2M]
┃ ┃ ┃ ┗━━强化学习基础_1-5.vep [254.3M]
┃ ┃ ┣━━Lecture：变分法2 [850.6M]
┃ ┃ ┃ ┣━━变分法-2-1.vep [521.5M]
┃ ┃ ┃ ┗━━变分法-2-2.vep [329.1M]
┃ ┃ ┗━━Review 强化学习基础 2 [904.6M]
┃ ┃ ┣━━强化学习基础_2-1.vep [362.8M]
┃ ┃ ┣━━强化学习基础_2-2.vep [359.9M]
┃ ┃ ┗━━强化学习基础_2-3.vep [181.9M]
┃ ┣━━第16周 强化学习基础-下 [3.9G]
┃ ┃ ┣━━Lecture 强化学习基础 3 [1.7G]
┃ ┃ ┃ ┣━━强化学习基础_3-1.vep [381.1M]
┃ ┃ ┃ ┣━━强化学习基础_3-2.vep [455.8M]
┃ ┃ ┃ ┣━━强化学习基础_3-3.vep [495.1M]
┃ ┃ ┃ ┗━━强化学习基础_3-4.vep [446.3M]
┃ ┃ ┣━━Paper Playing Atari with Deep Reinforcement Learning [948.2M]
┃ ┃ ┃ ┣━━Playing Atari with Deep Reinforcement Learning-1.vep [509.2M]
┃ ┃ ┃ ┗━━Playing Atari with Deep Reinforcement Learning-2.vep [439M]
┃ ┃ ┣━━Review 强化学习基础 4 [723.3M]
┃ ┃ ┃ ┣━━强化学习基础_4-1.vep [390.7M]
┃ ┃ ┃ ┗━━强化学习基础_4-2.vep [332.5M]
┃ ┃ ┗━━Review：项目布置 [496.9M]
┃ ┃ ┗━━项目布置.vep [496.9M]
┃ ┣━━第17周 自然语言处理中的RL [3.5G]
┃ ┃ ┣━━Lecture 自然语言处理中的RL [1.9G]
┃ ┃ ┃ ┣━━自然语言处理中的RL-1.vep [383.5M]
┃ ┃ ┃ ┣━━自然语言处理中的RL-2.vep [344.8M]
┃ ┃ ┃ ┣━━自然语言处理中的RL-3.vep [474.9M]
┃ ┃ ┃ ┣━━自然语言处理中的RL-4.vep [317.3M]
┃ ┃ ┃ ┗━━自然语言处理中的RL-5.vep [413M]
┃ ┃ ┣━━Review 强化学习于金融场景应用 [648.3M]
┃ ┃ ┃ ┗━━强化学习于金融场景应用.vep [648.3M]
┃ ┃ ┣━━Review 文本生成 + 强化学习 实战 [578.6M]
┃ ┃ ┃ ┗━━文本生成 + 强化学习 实战.vep [578.6M]
┃ ┃ ┗━━Review 作业二讲解 [458.7M]
┃ ┃ ┗━━作业二讲解.vep [458.7M]
┃ ┣━━第18周 Bandits [3.3G]
┃ ┃ ┣━━Lecture Bandits [2G]
┃ ┃ ┃ ┣━━Bandits-1.vep [488.6M]
┃ ┃ ┃ ┣━━Bandits-2.vep [455.5M]
┃ ┃ ┃ ┣━━Bandits-3.vep [257.9M]
┃ ┃ ┃ ┣━━Bandits-4.vep [419.6M]
┃ ┃ ┃ ┗━━Bandits-5.vep [385.5M]
┃ ┃ ┣━━Paper Master the game of Go without human knowledge [785.3M]
┃ ┃ ┃ ┣━━Master the game of Go without human knowledge-1.vep [408.7M]
┃ ┃ ┃ ┗━━Master the game of Go without human knowledge-2.vep [376.6M]
┃ ┃ ┗━━Review 作业三、四讲解 [618.1M]
┃ ┃ ┗━━作业三、四讲解.vep [618.1M]
┃ ┗━━第19周 就业指导 [763.2M]
┃ ┣━━就业指导-1.vep [602.4M]
┃ ┗━━就业指导-2.vep [160.9M]
┣━━00.资料.zip [384.7M]
┗━━vep加密播放说明.txt [204B]

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