【尊享】ZX049 – NLP企业项目实战训练营3期 [29G]

【尊享】ZX049 – NLP企业项目实战训练营3期 [29G]

┣━━01.视频 [26.5G]
┃ ┣━━就业指导 [1.2G]
┃ ┃ ┣━━就业指导1.vep [263.7M]
┃ ┃ ┣━━就业指导2.vep [470.1M]
┃ ┃ ┣━━项目在求职中的应用指导1.vep [308.4M]
┃ ┃ ┗━━项目在求职中的应用指导2.vep [200.6M]
┃ ┣━━week0 [1.6G]
┃ ┃ ┣━━开班典礼1.vep [225.1M]
┃ ┃ ┣━━开班典礼2.vep [160.8M]
┃ ┃ ┣━━开班典礼3.vep [607.2M]
┃ ┃ ┣━━开班典礼4.vep [226.1M]
┃ ┃ ┗━━开班典礼5.vep [436.7M]
┃ ┣━━week1 [3.9G]
┃ ┃ ┣━━20210130 Lecture [1.5G]
┃ ┃ ┃ ┣━━文本处理与特征工程1.vep [2.2M]
┃ ┃ ┃ ┣━━文本处理与特征工程2.vep [153.8M]
┃ ┃ ┃ ┣━━文本处理与特征工程3.vep [522.6M]
┃ ┃ ┃ ┣━━文本处理与特征工程4.vep [470M]
┃ ┃ ┃ ┗━━文本处理与特征工程5.vep [370.7M]
┃ ┃ ┗━━20210131 Lecture [2.5G]
┃ ┃ ┣━━20210131 Workshop1 [1.4G]
┃ ┃ ┃ ┣━━NLP工具的使用1.vep [495.9M]
┃ ┃ ┃ ┗━━NLP工具的使用2.vep [986.5M]
┃ ┃ ┣━━20210131 Workshop2 [558.4M]
┃ ┃ ┃ ┗━━如何阅读科研文章.vep [558.4M]
┃ ┃ ┗━━20210131 workshop3 [470M]
┃ ┃ ┗━━文本处理与特征工程.vep [470M]
┃ ┣━━week10 [2.1G]
┃ ┃ ┣━━20210424 Lecture [1.1G]
┃ ┃ ┃ ┣━━Learning to Rank1.vep [254.4M]
┃ ┃ ┃ ┣━━Learning to Rank2.vep [241.5M]
┃ ┃ ┃ ┣━━Learning to Rank3.vep [273.6M]
┃ ┃ ┃ ┗━━Learning to Rank4.vep [369.2M]
┃ ┃ ┣━━20210424 workshop [472M]
┃ ┃ ┃ ┗━━word moving distance paper 及代码.vep [472M]
┃ ┃ ┗━━20210509 Review [536.7M]
┃ ┃ ┗━━项目二任务3讲解.vep [536.7M]
┃ ┣━━week11 [2.6G]
┃ ┃ ┣━━20210515 Lecture11 [1.6G]
┃ ┃ ┃ ┣━━自注意力机制以及Transformer1.vep [315M]
┃ ┃ ┃ ┣━━自注意力机制以及Transformer2.vep [462.8M]
┃ ┃ ┃ ┣━━自注意力机制以及Transformer3.vep [487.7M]
┃ ┃ ┃ ┗━━自注意力机制以及Transformer4.vep [330M]
┃ ┃ ┣━━20210515 Workshop [477.7M]
┃ ┃ ┃ ┗━━Transformer 的实现及代码剖析.vep [477.7M]
┃ ┃ ┗━━20210516 Workshop [600.7M]
┃ ┃ ┣━━项目三的任务一1.vep [252M]
┃ ┃ ┗━━项目三的任务一2.vep [348.6M]
┃ ┣━━week12 [1.7G]
┃ ┃ ┣━━基于BERT和Transformer的闲聊引擎-1-.vep [406.2M]
┃ ┃ ┣━━基于BERT和Transformer的闲聊引擎-2-.vep [409.2M]
┃ ┃ ┣━━基于BERT和Transformer的闲聊引擎-3-.vep [346.2M]
┃ ┃ ┣━━基于BERT和Transformer的闲聊引擎-4-.vep [137.7M]
┃ ┃ ┗━━BERT的fine-tuning实例讲解-.vep [477.3M]
┃ ┣━━week13 [248.1M]
┃ ┃ ┣━━基于图的学习-1-.vep [90.4M]
┃ ┃ ┣━━基于图的学习-2-.vep [62.7M]
┃ ┃ ┗━━基于图的学习-3-.vep [95.1M]
┃ ┣━━week14 [883.1M]
┃ ┃ ┣━━代码课程一节.vep [204.9M]
┃ ┃ ┣━━基于图神经网络的Entity Linking-1.vep [79.6M]
┃ ┃ ┣━━基于图神经网络的Entity Linking-2.vep [208.7M]
┃ ┃ ┣━━基于图神经网络的Entity Linking-3.vep [135.3M]
┃ ┃ ┗━━项目任务讲解.vep [254.6M]
┃ ┣━━week15 [937.2M]
┃ ┃ ┣━━基于Bert-LSTM的命名实体识别-.vep [211.3M]
┃ ┃ ┣━━同类物品检索-.vep [391.8M]
┃ ┃ ┣━━GAT、GraphSage与Entity Linking-1-.vep [80.4M]
┃ ┃ ┣━━GAT、GraphSage与Entity Linking-2-.vep [90.1M]
┃ ┃ ┣━━GAT、GraphSage与Entity Linking-3-.vep [56M]
┃ ┃ ┗━━GAT、GraphSage与Entity Linking-4-.vep [107.5M]
┃ ┣━━week16 [633.5M]
┃ ┃ ┣━━同类检索项目.vep [187M]
┃ ┃ ┣━━图神经网络与其他应用.vep [75.2M]
┃ ┃ ┣━━Graphsage代码解读和实战1.vep [202.1M]
┃ ┃ ┗━━Graphsage代码解读和实战2.vep [169.3M]
┃ ┣━━week2 [1.1G]
┃ ┃ ┣━━20210206 Lecture [660.6M]
┃ ┃ ┃ ┣━━基于统计学习的分类方法1.vep [130.2M]
┃ ┃ ┃ ┣━━基于统计学习的分类方法2.vep [133.6M]
┃ ┃ ┃ ┣━━基于统计学习的分类方法3.vep [127.1M]
┃ ┃ ┃ ┣━━基于统计学习的分类方法4.vep [118M]
┃ ┃ ┃ ┗━━基于统计学习的分类方法5.vep [151.7M]
┃ ┃ ┗━━20210221 Lecture [433.2M]
┃ ┃ ┣━━处理样本的不平衡1.vep [139.8M]
┃ ┃ ┣━━Paperskipgram讲解1.vep [57.2M]
┃ ┃ ┣━━Paperskipgram讲解2.vep [109M]
┃ ┃ ┗━━Paperskipgram讲解3.vep [127.2M]
┃ ┣━━week3 [609.7M]
┃ ┃ ┣━━20210227 Lecture3 [237.4M]
┃ ┃ ┃ ┣━━基于深度 学习的分类方法1.vep [89.8M]
┃ ┃ ┃ ┣━━基于深度 学习的分类方法2.vep [48.9M]
┃ ┃ ┃ ┣━━基于深度学习的分类方法3.vep [38.8M]
┃ ┃ ┃ ┗━━基于深度学习的分类方法4.vep [59.9M]
┃ ┃ ┣━━20210228 Workshop1 [128.9M]
┃ ┃ ┃ ┣━━Pytorch的使用1.vep [122.3M]
┃ ┃ ┃ ┗━━Pytorch的使用2.vep [6.6M]
┃ ┃ ┗━━20210228 Workshop2 [243.4M]
┃ ┃ ┣━━项目作业中期讲解1.vep [71.3M]
┃ ┃ ┗━━项目作业中期讲解2.vep [172.2M]
┃ ┣━━week4 [1.3G]
┃ ┃ ┣━━20210306 Lecture4 [470M]
┃ ┃ ┃ ┣━━CNN与工业界模型部署1.vep [109.3M]
┃ ┃ ┃ ┣━━CNN与工业界模型部署2.vep [69.2M]
┃ ┃ ┃ ┣━━CNN与工业界模型部署3.vep [117.4M]
┃ ┃ ┃ ┗━━CNN与工业界模型部署4.vep [174.1M]
┃ ┃ ┣━━20210307 Workshop [329.2M]
┃ ┃ ┃ ┣━━模型的部署1.vep [152.4M]
┃ ┃ ┃ ┗━━模型的部署2.vep [176.8M]
┃ ┃ ┣━━20210307 Workshop1 [368.1M]
┃ ┃ ┃ ┣━━ResNet讲解1.vep [333.9M]
┃ ┃ ┃ ┗━━ResNet讲解2.vep [34.2M]
┃ ┃ ┗━━20210307 Workshop2 [200.9M]
┃ ┃ ┗━━第三次项目讲解.vep [200.9M]
┃ ┣━━week5 [685.8M]
┃ ┃ ┣━━20210313 Lecture5 [313.6M]
┃ ┃ ┃ ┣━━递归神经网络RNN与BPTT算法1.vep [65.3M]
┃ ┃ ┃ ┣━━递归神经网络RNN与BPTT算法2.vep [51.3M]
┃ ┃ ┃ ┣━━递归神经网络RNN与BPTT算法3.vep [132.4M]
┃ ┃ ┃ ┗━━递归神经网络RNN与BPTT算法4.vep [64.6M]
┃ ┃ ┗━━20210314 Workshop [372.2M]
┃ ┃ ┣━━实现基于LSTM的情感分类1.vep [131.2M]
┃ ┃ ┣━━实现基于LSTM的情感分类2.vep [79M]
┃ ┃ ┗━━实现基于LSTM的情感分类3.vep [162M]
┃ ┣━━week6 [885.1M]
┃ ┃ ┣━━20210320 Lecture6 [280.5M]
┃ ┃ ┃ ┣━━Seq2Seq模型与营销⽂本⽣成1.vep [54.9M]
┃ ┃ ┃ ┣━━Seq2Seq模型与营销⽂本⽣成2.vep [91.5M]
┃ ┃ ┃ ┗━━Seq2Seq模型与营销⽂本⽣成3.vep [134.1M]
┃ ┃ ┣━━20210321 Workshop1 [330.5M]
┃ ┃ ┃ ┣━━关于seq2seq的代码课1.vep [115.1M]
┃ ┃ ┃ ┣━━关于seq2seq的代码课2.vep [81.1M]
┃ ┃ ┃ ┗━━关于seq2seq的代码课3.vep [134.2M]
┃ ┃ ┗━━20210321 Workshop2 [274.1M]
┃ ┃ ┣━━项目二讲解1.vep [102.1M]
┃ ┃ ┗━━项目二讲解2.vep [172M]
┃ ┣━━week7 [1.5G]
┃ ┃ ┣━━20210327 Lecture7 [605.2M]
┃ ┃ ┃ ┣━━PointerGenerator Network和多模态识.vep [115.3M]
┃ ┃ ┃ ┣━━PointerGenerator Network和多模态识2.vep [204.2M]
┃ ┃ ┃ ┣━━PointerGenerator Network和多模态识3.vep [149.7M]
┃ ┃ ┃ ┗━━PointerGenerator Network和多模态识4.vep [135.9M]
┃ ┃ ┣━━20210327 Workshop1 [177.6M]
┃ ┃ ┃ ┗━━多模态的实现.vep [177.6M]
┃ ┃ ┣━━20210328 Workshop2 [427.9M]
┃ ┃ ┃ ┣━━代码实现 of PGN1.vep [286.8M]
┃ ┃ ┃ ┗━━代码实现 of PGN2.vep [141M]
┃ ┃ ┗━━20210328 Workshop3 [285.6M]
┃ ┃ ┣━━Project2项目教学1.vep [172M]
┃ ┃ ┗━━Project2项目教学2.vep [113.6M]
┃ ┣━━week8 [1.7G]
┃ ┃ ┣━━20210410 Lecture8 [693.1M]
┃ ┃ ┃ ┣━━对话系统技术概览以及深度学习训练技巧1.vep [103M]
┃ ┃ ┃ ┣━━对话系统技术概览以及深度学习训练技巧2.vep [161.8M]
┃ ┃ ┃ ┣━━对话系统技术概览以及深度学习训练技巧3.vep [64.7M]
┃ ┃ ┃ ┣━━对话系统技术概览以及深度学习训练技巧4.vep [175.9M]
┃ ┃ ┃ ┗━━对话系统技术概览以及深度学习训练技巧5.vep [187.7M]
┃ ┃ ┣━━20210411 Workshop1 [512.1M]
┃ ┃ ┃ ┣━━基于BM25，tfidf和SIF的检索系统实现1.vep [98.8M]
┃ ┃ ┃ ┗━━基于BM25，tfidf和SIF的检索系统实现2.vep [413.3M]
┃ ┃ ┗━━20210411 Workshop2 [547.8M]
┃ ┃ ┣━━项目二任务二讲解及任务三布置1.vep [359M]
┃ ┃ ┗━━项目二任务二讲解及任务三布置2.vep [188.8M]
┃ ┗━━week9 [2.9G]
┃ ┣━━20210417 Lecture9 [2.1G]
┃ ┃ ┣━━多轮对话管理1.vep [517.8M]
┃ ┃ ┣━━多轮对话管理2.vep [317.6M]
┃ ┃ ┣━━多轮对话管理3.vep [385.8M]
┃ ┃ ┣━━多轮对话管理4.vep [432.4M]
┃ ┃ ┗━━多轮对话管理5.vep [509.5M]
┃ ┣━━20210417 workshop1 [384.4M]
┃ ┃ ┗━━HNSW的代码实现.vep [384.4M]
┃ ┗━━20210418 workshop2 [467.6M]
┃ ┗━━多模态MMPG论文.vep [467.6M]
┣━━00.资料.zip [2.5G]
┗━━vep加密播放说明.txt [204B]

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