【尊享】ZX028 – NLP8期 [59.4G]

【尊享】ZX028 – NLP8期 [59.4G]

┣━━01.视频 [58G]
┃ ┣━━1任务1概论算法复杂度逻辑回归与正则1_.vep [318.3M]
┃ ┣━━2任务2概论算法复杂度逻辑回归与正则2_.vep [270.7M]
┃ ┣━━3任务3概论算法复杂度逻辑回归与正则3_.vep [177.9M]
┃ ┣━━4任务4概论算法复杂度逻辑回归与正则4_.vep [255.8M]
┃ ┣━━5任务5概论算法复杂度逻辑回归与正则5_.vep [196M]
┃ ┣━━6任务6概论算法复杂度逻辑回归与正则6_.vep [207.7M]
┃ ┣━━7任务7经典数据结构与算法之动态规划问题讲解1_.vep [268.8M]
┃ ┣━━8_任务8经典数据结构与算法之动态规划问题讲解2_.vep [276.7M]
┃ ┣━━9_任务9经典数据结构与算法之动态规划问题讲解3_.vep [390.7M]
┃ ┣━━10_任务10DecisionTreeRandomForestXGBoost1_.vep [353.5M]
┃ ┣━━11_任务11DecisionTreeRandomForestXGBoost2_.vep [345.4M]
┃ ┣━━12_任务12DecisionTreeRandomForestXGBoost3_.vep [332.1M]
┃ ┣━━13_任务13DecisionTreeRandomForestXGBoost4_.vep [155.2M]
┃ ┣━━14_任务14DecisionTreeRandomForestXGBoost5_.vep [307.8M]
┃ ┣━━15_任务15Ensemble模型实战1_.vep [267.6M]
┃ ┣━━16_任务16Ensemble模型实战2_.vep [179.8M]
┃ ┣━━17_任务17FromWordEmbeddingsToDocumentDistances1_.vep [283.2M]
┃ ┣━━18_任务18FromWordEmbeddingsToDocumentDistances2_.vep [309.5M]
┃ ┣━━19_任务19XGBoostAScalableTreeBoostingSystem_.vep [357.5M]
┃ ┣━━20_任务20生活中的优化问题1_.vep [308.8M]
┃ ┣━━21_任务21生活中的优化问题2_.vep [273.8M]
┃ ┣━━22_任务22凸优化11_.vep [147.5M]
┃ ┣━━23_任务23凸优化12_.vep [246.2M]
┃ ┣━━24_任务24凸优化13_.vep [327.5M]
┃ ┣━━25_任务25凸优化14_.vep [240.7M]
┃ ┣━━26_任务26凸优化15_.vep [325.1M]
┃ ┣━━27_任务27凸优化16_.vep [253.3M]
┃ ┣━━28_任务28SimplexMethod与LP实战1_.vep [404.7M]
┃ ┣━━29_任务29SimplexMethod与LP实战2_.vep [229.2M]
┃ ┣━━30_任务30SimplexMethod与LP实战3_.vep [236.9M]
┃ ┣━━31_任务31凸优化21_.vep [306.4M]
┃ ┣━━32_任务32凸优化22_.vep [366.9M]
┃ ┣━━33_任务33凸优化23_.vep [294.1M]
┃ ┣━━34_任务34LPQP以及它们的Dual1_.vep [192.9M]
┃ ┣━━35_任务35LPQP以及它们的Dual2_.vep [246.8M]
┃ ┣━━36_任务36Adamamethodforstochasticoptimization1_.vep [163.6M]
┃ ┣━━37_任务37Adamamethodforstochasticoptimization2_.vep [261.9M]
┃ ┣━━38_任务38文本表示1_.vep [148.7M]
┃ ┣━━39_任务39文本表示2_.vep [284.4M]
┃ ┣━━40_任务40文本表示3_.vep [346.5M]
┃ ┣━━41_任务41文本表示4_.vep [324.3M]
┃ ┣━━42_任务42文本表示5_.vep [266.8M]
┃ ┣━━43_任务43各类文本相似度计算技术的Survey1_.vep [299.1M]
┃ ┣━━44_任务44各类文本相似度计算技术的Survey2_.vep [170.3M]
┃ ┣━━45_任务45搜索引擎技术介绍1_.vep [167.7M]
┃ ┣━━46_任务46搜索引擎技术介绍2_.vep [373.6M]
┃ ┣━━47_任务47SkipGram重点讲解CBOWGloveMFGaussianEmbedding语言模型以及各类Smooting技术1_.vep [202.9M]
┃ ┣━━48_任务48SkipGram重点讲解CBOWGloveMFGaussianEmbedding语言模型以及各类Smooting技术2_.vep [273M]
┃ ┣━━49_任务49SkipGram重点讲解CBOWGloveMFGaussianEmbedding语言模型以及各类Smooting技术3_.vep [260.7M]
┃ ┣━━50_任务50SkipGram重点讲解CBOWGloveMFGaussianEmbedding语言模型以及各类Smooting技术4_.vep [242.4M]
┃ ┣━━51_任务51SkipGram重点讲解CBOWGloveMFGaussianEmbedding语言模型以及各类Smooting技术5_.vep [274.6M]
┃ ┣━━52_任务52SkipGram重点讲解CBOWGloveMFGaussianEmbedding语言模型以及各类Smooting技术6_.vep [217.9M]
┃ ┣━━53_任务53问答系统的搭建完整流程相似度匹配排序文本预处理等1_.vep [417.1M]
┃ ┣━━54_任务54问答系统的搭建完整流程相似度匹配排序文本预处理等2_.vep [226.9M]
┃ ┣━━55_任务55SkipGram源代码解读1_.vep [407.3M]
┃ ┣━━56_任务56SkipGram源代码解读2_.vep [417.6M]
┃ ┣━━57. 任务57：Evaluation methods for unsupervised word embeddings.vep [463.4M]
┃ ┣━━58. 任务58：EM算法和HMM-1.vep [294.6M]
┃ ┣━━59. 任务59：EM算法和HMM-2.vep [432M]
┃ ┣━━60. 任务60：EM算法和HMM-3.vep [340.1M]
┃ ┣━━61. 任务61：EM算法和HMM-4.vep [442.7M]
┃ ┣━━62. 任务62：EM算法和HMM-5.vep [397.9M]
┃ ┣━━63. 任务63：代码实战：如何基于HMM实现词性分析器？（POS tagger）-1.vep [212.3M]
┃ ┣━━64. 任务64：代码实战：如何基于HMM实现词性分析器？（POS tagger）-2.vep [429.5M]
┃ ┣━━65. 任务65：结巴分词的应用以及底层原理剖析-1.vep [212.1M]
┃ ┣━━66. 任务66：结巴分词的应用以及底层原理剖析-2.vep [273.7M]
┃ ┣━━67. 任务67：Bidirectional LSTM-CRF Models for Sequence Tagging.vep [340.6M]
┃ ┣━━68. 任务68：CRF模型-1.vep [238.4M]
┃ ┣━━69. 任务69：CRF模型-2.vep [247.5M]
┃ ┣━━70. 任务70：CRF模型-3.vep [344M]
┃ ┣━━71. 任务71：CRF模型-4.vep [239.7M]
┃ ┣━━72. 任务72：CRF模型-5.vep [215.4M]
┃ ┣━━73. 任务73：项目一讲解-1.vep [365.2M]
┃ ┣━━74. 任务74：项目一讲解-2.vep [483.2M]
┃ ┣━━75. 任务75-代码实战：基于LSTM-CRF的命名实体识别.vep [512.6M]
┃ ┣━━76_任务76RNNLSTM模型可视化1_.vep [428.6M]
┃ ┣━━77_任务77RNNLSTM模型可视化2_.vep [342.4M]
┃ ┣━━78_任务78RNNLSTM模型可视化3_.vep [413.8M]
┃ ┣━━79_任务79RNNLSTM模型可视化4_.vep [463.8M]
┃ ┣━━80_任务80RNNLSTM模型可视化5_.vep [227.3M]
┃ ┣━━81_任务81Pytorch讲解1_.vep [596.4M]
┃ ┣━━82_任务82Pytorch讲解2_.vep [308.2M]
┃ ┣━━83任务83Word2vec重点回顾1_.vep [217.9M]
┃ ┣━━84_任务84Word2vec重点回顾2_.vep [252.6M]
┃ ┣━━85_任务85GPU的使用与环境搭建基于pytorch的简单的神经网络搭建1_.vep [341M]
┃ ┣━━86_任务86GPU的使用与环境搭建基于pytorch的简单的神经网络搭建2_.vep [149.8M]
┃ ┣━━87_任务87第一二次小作业讲解1_.vep [334M]
┃ ┣━━88_任务88第一二次小作业讲解2_.vep [357.1M]
┃ ┣━━89_任务89Seq2SeqAttentionPointerNetwork1_.vep [368.6M]
┃ ┣━━90_任务90Seq2SeqAttentionPointerNetwork2_.vep [436.2M]
┃ ┣━━91_任务91Seq2SeqAttentionPointerNetwork3_.vep [322.5M]
┃ ┣━━92_任务92Seq2SeqAttentionPointerNetwork4_.vep [430.2M]
┃ ┣━━93_任务93Seq2SeqAttentionPointerNetwork5_.vep [482.1M]
┃ ┣━━94_任务94IntroductiontoTransferLearing1_.vep [276.8M]
┃ ┣━━95_任务95IntroductiontoTransferLearing2_.vep [408.9M]
┃ ┣━━96_任务96SequencetoSequenceLearningwithNeuralNetworks_.vep [351.2M]
┃ ┣━━97_任务97文本摘要1_.vep [257.3M]
┃ ┣━━98_任务98文本摘要2_.vep [378M]
┃ ┣━━99_任务99文本摘要3_.vep [360.9M]
┃ ┣━━100_任务100文本摘要4_.vep [411.9M]
┃ ┣━━101_任务101文本摘要5_.vep [548.6M]
┃ ┣━━102_任务102LSTM的实现源码讲解1_.vep [272M]
┃ ┣━━103_任务103LSTM的实现源码讲解2_.vep [319M]
┃ ┣━━104_任务104GetToThePoint_.vep [530.1M]
┃ ┣━━105_任务105TransformerBERT1_.vep [467.6M]
┃ ┣━━106_任务106TransformerBERT2_.vep [540.6M]
┃ ┣━━107_任务107TransformerBERT3_.vep [392.1M]
┃ ┣━━108_任务108TransformerBERT4_.vep [521.7M]
┃ ┣━━109_任务109TransformerBERT5_.vep [348.2M]
┃ ┣━━110_任务110BERT的训练与实战1_.vep [497.1M]
┃ ┣━━111_任务111BERT的训练与实战2_.vep [416.6M]
┃ ┣━━112_任务112基于Transformer的机器翻译1_.vep [372.1M]
┃ ┣━━113_任务113基于Transformer的机器翻译2_.vep [424.1M]
┃ ┣━━114_任务114BERTPretrainingofDeepBidirectionalTransformersforLanguageUnderstanding_.vep [231.1M]
┃ ┣━━115_任务115Lecture基于transformer的其他预训练模型GPTXLNetMASSBART1_.vep [358.5M]
┃ ┣━━116_任务116Lecture基于transformer的其他预训练模型GPTXLNetMASSBART2_.vep [568.1M]
┃ ┣━━117_任务117Lecture基于transformer的其他预训练模型GPTXLNetMASSBART3_.vep [515.7M]
┃ ┣━━118_任务118Lecture基于transformer的其他预训练模型GPTXLNetMASSBART4_.vep [427.9M]
┃ ┣━━119_任务119Lecture基于transformer的其他预训练模型GPTXLNetMASSBART5_.vep [451M]
┃ ┣━━120_任务120reviewXLNET应用在文本分类1_.vep [290.1M]
┃ ┣━━121_任务121reviewXLNET应用在文本分类2_.vep [183M]
┃ ┣━━122_任务122review项目二讲解1_.vep [428.1M]
┃ ┣━━123_任务123review项目二讲解2_.vep [203.2M]
┃ ┣━━124任务124Lecture任务型对话系统开放领域对话系统1_.vep [310.2M]
┃ ┣━━125任务125Lecture任务型对话系统开放领域对话系统2_.vep [471.9M]
┃ ┣━━126任务126Lecture任务型对话系统开放领域对话系统3_.vep [391.6M]
┃ ┣━━127任务127Lecture任务型对话系统开放领域对话系统4_.vep [363.8M]
┃ ┣━━128_任务128Lecture任务型对话系统开放领域对话系统5_.vep [360M]
┃ ┣━━129任务129review对话系统中的nlg1_.vep [291.6M]
┃ ┣━━130任务130review对话系统中的nlg2_.vep [315.8M]
┃ ┣━━131任务131review对话系统中的nlu1_.vep [380.3M]
┃ ┣━━132任务132review对话系统中的nlu2_.vep [594.6M]
┃ ┣━━133任务133paperEfficientLargeScaleDomainClassificationwithPersonalizedAttention_.vep [396.4M]
┃ ┣━━134任务134Lecture模型压缩低资源学习1_.vep [332.8M]
┃ ┣━━135任务135Lecture模型压缩低资源学习2_.vep [432.1M]
┃ ┣━━136任务136Lecture模型压缩低资源学习3_.vep [583M]
┃ ┣━━137任务137Review论文阅读ALBert_.vep [240.5M]
┃ ┣━━138任务138Lecture信息抽取1_.vep [253.1M]
┃ ┣━━139任务139Lecture信息抽取2_.vep [150.1M]
┃ ┣━━140任务140Lecture信息抽取3_.vep [270.5M]
┃ ┣━━141任务141Lecture信息抽取4_.vep [304.3M]
┃ ┣━━142任务142Lecture信息抽取5_.vep [313.5M]
┃ ┣━━143任务143Review低资源下NLP应用1_.vep [280M]
┃ ┣━━144任务144Review低资源下NLP应用2_.vep [199.8M]
┃ ┣━━145任务145Review项目三机器翻译项目讲解1_.vep [382.5M]
┃ ┣━━146任务146Review项目三机器翻译项目讲解2_.vep [165.1M]
┃ ┣━━147任务147Lecture信息抽取21_.vep [246.5M]
┃ ┣━━148任务148Lecture信息抽取22_.vep [284M]
┃ ┣━━149任务149Lecture信息抽取23_.vep [392.3M]
┃ ┣━━150任务150Lecture信息抽取24_.vep [437.5M]
┃ ┣━━151任务151Lecture信息抽取25_.vep [345.1M]
┃ ┣━━152任务152Lecture信息抽取26_.vep [255.5M]
┃ ┣━━153任务153Review命名实体识别代码实战BERTBILSTMCRF1_.vep [415M]
┃ ┣━━154任务154Review命名实体识别代码实战BERTBILSTMCRF2_.vep [279.3M]
┃ ┣━━155任务155Lecture知识图谱1_.vep [543M]
┃ ┣━━156任务156Lecture知识图谱2_.vep [530.6M]
┃ ┣━━157任务157Review知识图谱在推荐系统中的应用1_.vep [296.5M]
┃ ┣━━158任务158Review知识图谱在推荐系统中的应用2_.vep [541.9M]
┃ ┣━━159任务159review项目四讲解_.vep [383.6M]
┃ ┣━━160任务160Lecture图神经网络1_.vep [628.1M]
┃ ┣━━161任务161Lecture图神经网络2_.vep [624M]
┃ ┣━━162任务162Lecture图神经网络3_.vep [359.3M]
┃ ┣━━163任务163Lecture图神经网络4_.vep [645.8M]
┃ ┣━━164任务164Review项目五讲解_.vep [597.8M]
┃ ┣━━165任务165Lecture概率图模型1_.vep [553.9M]
┃ ┣━━166任务166Lecture概率图模型2_.vep [211.8M]
┃ ┣━━167任务167Lecture概率图模型3_.vep [325.4M]
┃ ┣━━168任务168Lecture概率图模型4_.vep [359.8M]
┃ ┣━━169任务169ReviewBayesianneuralnetwork_.vep [603.8M]
┃ ┣━━170任务170ReviewMCMC之MetroplisHasting算法1_.vep [569.8M]
┃ ┣━━171任务171ReviewMCMC之MetroplisHasting算法2_.vep [382.7M]
┃ ┣━━172任务172review就业指导1_.vep [323.3M]
┃ ┗━━173任务173review就业指导2_.vep [337.8M]
┗━━00.资料.zip [1.5G]

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