NLP论文会员2021包括2022年1月前NLP论文班所有内容 部分资料文件夹未循环遍历 大小非实际大小

【更新】【尊享】ZX011 – NLP论文会员2021 [107.9G]

┣━━0.选修 [254.6M]
┃ ┣━━神经网络基础 [70.6M]
┃ ┃ ┣━━01.视频
┃ ┃ ┗━━00.资料.zip [70.6M]
┃ ┗━━NLP基础知识 [184.1M]
┃ ┣━━01.视频
┃ ┗━━00.资料.zip [184.1M]
┣━━1.NLP-BASELINE [7G]
┃ ┣━━01.视频 [7G]
┃ ┃ ┣━━1.NLP baseline 开营仪式.vep [140.1M]
┃ ┃ ┣━━2.01-word2vec-研究背景-01.vep [72.8M]
┃ ┃ ┣━━3.01-word2vec-研究成果意义-02.vep [62M]
┃ ┃ ┣━━4.01-word2vec-对比模型-03.vep [54.6M]
┃ ┃ ┣━━5.01-word2vec-对比模型-04.vep [32.5M]
┃ ┃ ┣━━6.01-word2vec-关键技术-05.vep [43M]
┃ ┃ ┣━━7.01-word2vec-模型复杂度-06.vep [20.2M]
┃ ┃ ┣━━8.01-word2vec-实验结果-07.vep [54.7M]
┃ ┃ ┣━━9.01-word2vec-skip-gram实现-08.vep [108.5M]
┃ ┃ ┣━━10.01-word2vec-cbow实现-09.vep [176.3M]
┃ ┃ ┣━━11.02glove-01-_背景介绍..vep [64.3M]
┃ ┃ ┣━━12.02 glove-02-_研究成果及意义.vep [34.6M]
┃ ┃ ┣━━13.02glove-03-论文概述.vep [123.1M]
┃ ┃ ┣━━14.02glove-04-模型精讲.vep [106.2M]
┃ ┃ ┣━━15.02 glove-05-实验分析..vep [48.4M]
┃ ┃ ┣━━16.02glove-06-数据处理.vep [61.1M]
┃ ┃ ┣━━17.02 glove-07-型及训练测试.vep [65.7M]
┃ ┃ ┣━━18.03char_embedding-01-背景介绍..vep [75.1M]
┃ ┃ ┣━━19.03 char_embedding-02-研究成果及意义.vep [56.6M]
┃ ┃ ┣━━20.03char_embedding-03-论文概述.vep [70.7M]
┃ ┃ ┣━━21.03 char_embedding-04-模型详解.vep [108.4M]
┃ ┃ ┣━━22.03 char_embedding-05-语言模型实验分析.vep [89.4M]
┃ ┃ ┣━━23.03 char_embedding-06-词性标注实验分析及论文总结.vep [114.1M]
┃ ┃ ┣━━24.03 char_embedding-07-环境配置.vep [72.2M]
┃ ┃ ┣━━25.03 char_embedding-08-数据处理.vep [137.3M]
┃ ┃ ┣━━26.03 char_embedding-09-模型构建及训练和测试.vep [91.1M]
┃ ┃ ┣━━27.04textcnn-01-textcnn背景介绍.vep [39M]
┃ ┃ ┣━━28.04textcnn-02-textcnn研究成果及意义.vep [25.9M]
┃ ┃ ┣━━29.04 textcnn-03-textcnn模型简介.vep [90.1M]
┃ ┃ ┣━━30.04 textcnn-04-textcnn模型详解.vep [87.5M]
┃ ┃ ┣━━31.04textcnn-05-textcnn实验介绍.vep [147.3M]
┃ ┃ ┣━━32.04 textcnn-06-textcnn超参选择.vep [232.7M]
┃ ┃ ┣━━33.04 testcnn-07-textcnn数据处理以及模型构建..vep [138M]
┃ ┃ ┣━━34.04 testcnn-08-textcnn训练及测试.vep [111.4M]
┃ ┃ ┣━━35.05-chartextcnn_1_论文导读..vep [85.1M]
┃ ┃ ┣━━36.05-chartextcnn_2_1_模型总览及简介.vep [112.5M]
┃ ┃ ┣━━37.05-chartextcnn_2_2_模型详解.vep [91.9M]
┃ ┃ ┣━━38.05-chartextcnn_2_3_实验分析及讨论.vep [111.1M]
┃ ┃ ┣━━39.05-chartextcnn_3_1_数据处理.vep [98.5M]
┃ ┃ ┣━━40.05-chartextcnn_3_2_模型定义及训练和测试.vep [121.5M]
┃ ┃ ┣━━41.06-fasttext_1_研究背景及意义.vep [77.2M]
┃ ┃ ┣━━42.06-fasttext_2_1_fasttext模型上.vep [80.8M]
┃ ┃ ┣━━43.06-fasttext_2_2_fasttext模型下.vep [83.2M]
┃ ┃ ┣━━44.06-fasttext_2_3_fasttext实验.vep [55.4M]
┃ ┃ ┣━━45.06-fasttext_3_1_fasttext数据读取.vep [105.6M]
┃ ┃ ┣━━46.06-fasttext_3_2_fasttext模型及训练测试.vep [56.5M]
┃ ┃ ┣━━47.07 deep_nmt_1_1_论文简介以及BLEU介绍.vep [48.8M]
┃ ┃ ┣━━48.07 deep_nmt_1_2_背景介绍和研究成果及意义.vep [93.2M]
┃ ┃ ┣━━49.07 deep_nmt_2_1_deep_nmt模型详解1.vep [94.2M]
┃ ┃ ┣━━50.07 deep_nmt_2_2_deep_nmtm模型详解2.vep [83.4M]
┃ ┃ ┣━━51.07 deep_nmt_2_3_实验结果及总结.vep [85.4M]
┃ ┃ ┣━━52.07 deep_nmt_3_1_机器翻译数据处理和代码简介.vep [133.2M]
┃ ┃ ┣━━53.07 deep_nmt_3_2_模型和训练及测试.vep [123.6M]
┃ ┃ ┣━━54.attention_nmt_1_1_储备知识_对齐翻译_seq2seq_注意力机制..vep [63.6M]
┃ ┃ ┣━━55.attention_nmt_1_2_背景介绍_研究成果及意义.vep [86.8M]
┃ ┃ ┣━━56.attention_nmt_2_1_论文总览..vep [98.5M]
┃ ┃ ┣━━57.attention_nmt_2_2模型详解..vep [99.2M]
┃ ┃ ┣━━58.attention_nmt_2_3_实验结果及分析.vep [109M]
┃ ┃ ┣━━59.attention_nmt_3_1_deep_nmt实现.vep [251.9M]
┃ ┃ ┣━━60.attention_nmt_3_2_fairseq.vep [174.9M]
┃ ┃ ┣━━61.han_attention_1_1_前期储备知识介绍.vep [48.9M]
┃ ┃ ┣━━62.han_attention_1_2_研究背景成果及意义..vep [87.9M]
┃ ┃ ┣━━63.han_attention_2_1_论文总览.vep [125.7M]
┃ ┃ ┣━━64.han_attention_2_2_模型详解.vep [86.6M]
┃ ┃ ┣━━65.han_attention_2_3_实验结果及论文总结.vep [235.3M]
┃ ┃ ┣━━66.han_attention_3_1_数据读取.vep [147.3M]
┃ ┃ ┣━━67.han_attention_3_2_模型实现及训练和测试.vep [153.6M]
┃ ┃ ┣━━68.sgm_1_1_多标签分类介绍..vep [37.9M]
┃ ┃ ┣━━69.sgm_1_2_背景知识和研究成果及意义.vep [107.3M]
┃ ┃ ┣━━70.sgm_2_1_论文简介.vep [91.8M]
┃ ┃ ┣━━71.sgm_2_2_模型详解..vep [63.9M]
┃ ┃ ┣━━72.sgm_2_3_实验结果及分析.vep [109M]
┃ ┃ ┣━━73.sgm_3_1_数据处理.vep [133.5M]
┃ ┃ ┗━━74.sgm_3_2_模型实现..vep [206.1M]
┃ ┗━━02.资料 [0B]
┃ ┣━━01 Word2vec
┃ ┣━━02 Glove
┃ ┣━━03 C2w
┃ ┣━━04 textcnn
┃ ┣━━05 Chartext.cnn
┃ ┣━━06fasttext
┃ ┣━━07deep_nmt
┃ ┣━━08 attention-nmt
┃ ┣━━09 han_attention
┃ ┣━━10 sgm
┃ ┗━━开营课件
┣━━2.NLP-预训练模型 [12.5G]
┃ ┣━━01.视频 [12.5G]
┃ ┃ ┣━━1.01transformer-01-论文背景&研究成果.vep [86.2M]
┃ ┃ ┣━━2.01transformer-02-attention回顾.vep [81.4M]
┃ ┃ ┣━━3.01transformer-03-模型框架和self_attention.vep [79.6M]
┃ ┃ ┣━━4.01transformer-04-模型小trick..vep [168.8M]
┃ ┃ ┣━━5.01transformer-05-代码框架部分和encoder.vep [254.3M]
┃ ┃ ┣━━6.01transformer-06-代码decoder和self_attention.vep [235M]
┃ ┃ ┣━━7.01transformer-07-代码训练部分和预测部分.vep [329.7M]
┃ ┃ ┣━━8.02transformer_xl-01-论文背景..vep [117.3M]
┃ ┃ ┣━━9.02transformer_xl-02-vallini model回顾..vep [89.9M]
┃ ┃ ┣━━10.02transformer_xl-03-片段级递归机制..vep [69.3M]
┃ ┃ ┣━━11.02transformer_xl-04-相对位置编码和小trick..vep [76.4M]
┃ ┃ ┣━━12.02transformer_xl-05-论文总结..vep [175.8M]
┃ ┃ ┣━━13.02transformerxl-06-代码数据准备..vep [122.2M]
┃ ┃ ┣━━14.02transformerxl-07-代码self attention..vep [293M]
┃ ┃ ┣━━15.02transformer_xl-08-代码update memory和adaptive.vep [206.9M]
┃ ┃ ┣━━16.02transformer_xl-09-代码adaptive softmax2..vep [267M]
┃ ┃ ┣━━17.03elmo-01-elmo的下游任务介绍..vep [100.6M]
┃ ┃ ┣━━18.03elmo-02-feature_based和fine_tuning.vep [80.4M]
┃ ┃ ┣━━19.03elmo-03-word2vec和charcnn回顾.vep [49.1M]
┃ ┃ ┣━━20.03elmo-04-Bidirectional_language_models.vep [57.7M]
┃ ┃ ┣━━21.03elmo-05-how to use emol..vep [50.8M]
┃ ┃ ┣━━22.03elmo-06-论文回顾..vep [117.2M]
┃ ┃ ┣━━23.03elmo-07-代码预处理部分.vep [242.9M]
┃ ┃ ┣━━24.03elmo-08-代码模型结构部分.vep [218.7M]
┃ ┃ ┣━━25.03elmo-09-代码crf流程..vep [163.5M]
┃ ┃ ┣━━26.03elmo-10-代码crf实现..vep [233.3M]
┃ ┃ ┣━━27.04gpt-01-nlp下游任务介绍.vep [128.4M]
┃ ┃ ┣━━28.04gpt-02-transformer回顾.vep [96.8M]
┃ ┃ ┣━━29.04gpt-03-预训练和fine-tuning.vep [63.5M]
┃ ┃ ┣━━30.04gpt-04-输入转换.vep [48.5M]
┃ ┃ ┣━━31.04gpt-05-论文回顾..vep [105.7M]
┃ ┃ ┣━━32.04gpt-06-代码流程和建立vocab.vep [148.5M]
┃ ┃ ┣━━33.04gpt-07-代码与处理部分.vep [172.1M]
┃ ┃ ┣━━34.04gpt-08-代码trasform_roc部分.vep [80.6M]
┃ ┃ ┣━━35.04gpt-09-代码transformer_model部分.vep [173.5M]
┃ ┃ ┣━━36.04gpt-10-代码两种loss的计算.vep [132.2M]
┃ ┃ ┣━━37.04gpt-11-代码训练部分.vep [120.9M]
┃ ┃ ┣━━38.05bert-01-bert的背景和glue benchmark..vep [105.7M]
┃ ┃ ┣━━39.05bert-02-论文导读和bert 衍生模型..vep [89M]
┃ ┃ ┣━━40.05bert-03-bert、gtp、elmo的比较.vep [38M]
┃ ┃ ┣━━41.05bert-04-bert model和pre-training部分.vep [75M]
┃ ┃ ┣━━42.05bert-05-bert的fine-tuning部分.vep [54.1M]
┃ ┃ ┣━━43.05bert-06-代码fine-tuning数据预处理和model 加载.vep [140.1M]
┃ ┃ ┣━━44.05bert-07-代码fine-tuning训练部分.vep [66.3M]
┃ ┃ ┣━━45.05bert-08-代码bert pretrain的NSP.vep [127.8M]
┃ ┃ ┣━━46.05bert-09-代码pertrain预处理.vep [182.8M]
┃ ┃ ┣━━47.05bert-10-代码bert-pretrain的transformer部分..vep [167.9M]
┃ ┃ ┣━━48.05bert-11-代码bert pretrain的loss计算..vep [159.6M]
┃ ┃ ┣━━49.06ulmfit-01-uimfit背景介绍.vep [128.2M]
┃ ┃ ┣━━50.06ulmfit-02-awdLstm回顾..vep [50M]
┃ ┃ ┣━━51.06ulmfit-03-下三角学习率.vep [54.7M]
┃ ┃ ┣━━52.06ulmfit-04-classifier fine tuning..vep [46.1M]
┃ ┃ ┣━━53.06ulmfit-05-论文回顾.vep [112.2M]
┃ ┃ ┣━━54.06ulmfit-06-代码fine tuning部分.vep [171.2M]
┃ ┃ ┣━━55.06ulmfit-07-代码逐层解冻和预测.vep [95M]
┃ ┃ ┣━━56.06ulmfit-08-代码pycharm lm部分..vep [166.9M]
┃ ┃ ┣━━57.07albert-01-albert背景介绍.vep [85.2M]
┃ ┃ ┣━━58.07albert-02-轻量级bert回顾.vep [64.3M]
┃ ┃ ┣━━59.07albert-03-embedding layer的因式分解.vep [83M]
┃ ┃ ┣━━60.07albert-04-albert跨层参数共享.vep [38.4M]
┃ ┃ ┣━━61.07albert-05-NSP任务和论文回顾..vep [177.4M]
┃ ┃ ┣━━62.07albert-06-代码tokenizer部分.vep [97.8M]
┃ ┃ ┣━━63.07albert-07-代码samplemask.vep [161.1M]
┃ ┃ ┣━━64.07albert-08-代码transformer结构.vep [180.1M]
┃ ┃ ┣━━65.07albert-09-代码pretrain 训练部分.vep [86M]
┃ ┃ ┣━━66.07albert-10-代码albert fine-tuning.vep [350.2M]
┃ ┃ ┣━━67.08mass-01-mass背景介绍..vep [126.3M]
┃ ┃ ┣━━68.08mass-02-bert和gpt回顾..vep [84.4M]
┃ ┃ ┣━━69.08mass-03-mass 的seq2seq pretraining..vep [93.4M]
┃ ┃ ┣━━70.08mass-04-mass的discussions..vep [224.1M]
┃ ┃ ┣━━71.08mass-05-代码fairseq的训练流程..vep [207.1M]
┃ ┃ ┣━━72.08mass-06-代码mass的xseq2seq部分.vep [390.1M]
┃ ┃ ┣━━73.08mass-07-代码mass的xtransformer部分..vep [173.8M]
┃ ┃ ┣━━74.08mass-08-代码mass的dataset准备..vep [215.9M]
┃ ┃ ┣━━75.09xlnet-01-xlnet背景介绍..vep [63.3M]
┃ ┃ ┣━━76.09xlnet-02-AR和AE的比较..vep [87.9M]
┃ ┃ ┣━━77.09xlnet-03-排列lm部分..vep [67.3M]
┃ ┃ ┣━━78.09xlnet-04-排列lm的mask实现.vep [56.7M]
┃ ┃ ┣━━79.09xlnet-05-传统lm存在的问题..vep [46.5M]
┃ ┃ ┣━━80.09xlnet-06-Two Stream Self-attention..vep [81.9M]
┃ ┃ ┣━━81.09xlnet-07-xlnet论文回顾.vep [134.3M]
┃ ┃ ┣━━82.09xlnet-08-代码xlnet的fine-tuning..vep [151.8M]
┃ ┃ ┣━━83.09xlnet-09-代码xlnet的mask..vep [422.8M]
┃ ┃ ┣━━84.09xlnet-10-代码xlnet的self attention..vep [294.6M]
┃ ┃ ┣━━85.10electra-01-electra背景介绍..vep [77.3M]
┃ ┃ ┣━━86.10electra-02-gan的回顾..vep [66.1M]
┃ ┃ ┣━━87.10electra-03-electra的生成器和判别器详解..vep [52.6M]
┃ ┃ ┣━━88.10electra-04-论文回顾..vep [122.3M]
┃ ┃ ┣━━89.10electra-05-代码electra训练流程..vep [218.8M]
┃ ┃ ┣━━90.10electra-06-代码预处理部分..vep [269.8M]
┃ ┃ ┣━━91.10electra-07-代码生成器和判别器..vep [289.8M]
┃ ┃ ┗━━92.10electra-08-代码start training部分..vep [231.8M]
┃ ┗━━02.资料 [0B]
┃ ┣━━01transformer_非视频
┃ ┣━━02transformer_xl_非视频
┃ ┣━━03elmo
┃ ┣━━04gpt
┃ ┣━━05bert
┃ ┣━━06 ulmfit
┃ ┣━━07albert
┃ ┣━━08 mass
┃ ┣━━09 xlnet
┃ ┣━━10electra
┃ ┗━━论文原文
┣━━3.信息抽取-命名体识别 [3.1G]
┃ ┣━━01.视频 [3.1G]
┃ ┃ ┣━━1.01- BiLSTM-CRF-论文研究背景.vep [130M]
┃ ┃ ┣━━2.02- BiLSTM-CRF-论文算法总览.vep [92.2M]
┃ ┃ ┣━━3.03-BiLSTM-CRF模型结构.vep [73.2M]
┃ ┃ ┣━━4.04-BiLSTM-CRF损失函数与维特比解码.vep [56.3M]
┃ ┃ ┣━━5.05- BiLSTM-CRF-实验结果与论文总结.vep [35.2M]
┃ ┃ ┣━━6.06- BiLSTM-CRF代码讲解.vep [180.9M]
┃ ┃ ┣━━7.07- BiLSTM-CRF-NCR-Fpp代码详解.vep [155.8M]
┃ ┃ ┣━━8.01_LatticeLSTM论文研究背景.vep [154.8M]
┃ ┃ ┣━━9.02_LatticeLSTM模型总览..vep [32.9M]
┃ ┃ ┣━━10.03_LatticeLSTM模型细节.vep [61.4M]
┃ ┃ ┣━━11.04_LatticeLSTM论文实验与总结.vep [26.4M]
┃ ┃ ┣━━12.05_LatticeLSTM代码讲解..vep [322.9M]
┃ ┃ ┣━━13.01_LR-CNN论文研究背景.vep [127M]
┃ ┃ ┣━━14.02_LR-CNN模型总览.vep [61.9M]
┃ ┃ ┣━━15.03_LR-CNN模型细节.vep [50.6M]
┃ ┃ ┣━━16.04_LR-CNN模型细节2..vep [35.1M]
┃ ┃ ┣━━17.05_LR-CNN论文代码讲解..vep [162M]
┃ ┃ ┣━━18.01_LGN论文研究背景..vep [149.1M]
┃ ┃ ┣━━19.02_LGN模型总览..vep [30.9M]
┃ ┃ ┣━━20.03_LGN模型详解.vep [43.1M]
┃ ┃ ┣━━21.04_LGN代码讲解.vep [87.8M]
┃ ┃ ┣━━22.01_TENER论文研究背景.vep [199.8M]
┃ ┃ ┣━━23.02_TENER模型总览.vep [74.3M]
┃ ┃ ┣━━24.03_TENER模型详解.vep [101.3M]
┃ ┃ ┣━━25.04_TENER模型总结.vep [42.6M]
┃ ┃ ┣━━26.05_TENER模型代码.vep [169.2M]
┃ ┃ ┣━━27.6-1_Soft_Lexicon论文研究背景..vep [185.8M]
┃ ┃ ┣━━28.6-2_Soft_Lexicon模型总览.vep.vep [40.9M]
┃ ┃ ┣━━29.6-3_Soft_Lexicon模型详解..vep [35M]
┃ ┃ ┣━━30.6-4_Soft_Lexicon模型总结..vep [88.2M]
┃ ┃ ┗━━31.6-5_Soft_Lexicon模型代码..vep [121.1M]
┃ ┗━━02.资料 [0B]
┃ ┣━━01 信息抽取
┃ ┣━━02
┃ ┣━━03
┃ ┣━━04
┃ ┣━━05
┃ ┣━━06
┃ ┗━━论文pdf
┣━━4.信息抽取-关系抽取 [6.6G]
┃ ┣━━01.视频 [6.6G]
┃ ┃ ┣━━1.1cnn关系识别-背景介绍.vep [311.4M]
┃ ┃ ┣━━1.2cnn关系识别-模型详解.vep [278.1M]
┃ ┃ ┣━━1.3cnn关系识别-代码实战.vep [410.6M]
┃ ┃ ┣━━2-1 crcnn与pcnn-背景介绍.vep [545.4M]
┃ ┃ ┣━━2.2 crcnn与pcnn-模型详解.vep [903.1M]
┃ ┃ ┣━━2.3 crcnn与pcnn-代码实战.vep [570.6M]
┃ ┃ ┣━━3.1 att-blstm 关系识别-背景介绍.vep [234.3M]
┃ ┃ ┣━━3.2 att-blstm 关系识别-模型详解.vep [327M]
┃ ┃ ┣━━3.3 att-blstm 关系识别-代码实战.vep [457.6M]
┃ ┃ ┣━━4.1 LSTM-LSTM-bias 背景介绍.vep [258.6M]
┃ ┃ ┣━━4.2 LSTM-LSTM-bias模型详解.vep [406.5M]
┃ ┃ ┣━━4.3 LSTM-LSTM-bias代码实战.vep [710.4M]
┃ ┃ ┣━━5.1 CasRel 背景介绍.vep [235.3M]
┃ ┃ ┣━━5.2 CasRel模型详解.vep [447.7M]
┃ ┃ ┗━━5.3 CasRel代码实战.vep [635.2M]
┃ ┗━━02.资料 [0B]
┃ ┣━━源码
┃ ┣━━paper1
┃ ┣━━paper2
┃ ┣━━paper3
┃ ┣━━paper4
┃ ┗━━paper5
┣━━5.图神经网络 [22.3G]
┃ ┣━━01.视频 [22.3G]
┃ ┃ ┣━━1.00图神经网络专题-01-开班课..vep [128.5M]
┃ ┃ ┣━━2.00图神经网络专题-02-开班课.vep [71.6M]
┃ ┃ ┣━━3.01第一次直播答疑..vep [130.7M]
┃ ┃ ┣━━4.02第二次直播答疑..vep [145.3M]
┃ ┃ ┣━━5.03第三次直播答疑..vep [106.7M]
┃ ┃ ┣━━6.01nodevec-01-研究背景.vep [46.9M]
┃ ┃ ┣━━7.01nodevec-02-研究成果.vep [109.5M]
┃ ┃ ┣━━8.01nodevec-03-图的应用.vep [66.4M]
┃ ┃ ┣━━9.01nodevec-04-模型结构&BFS&DFS.vep [242.7M]
┃ ┃ ┣━━10.01nodevec-05-模型算法&alias算法.vep [400.9M]
┃ ┃ ┣━━11.01nodevec-06-实验分析.vep [334.6M]
┃ ┃ ┣━━12.01nodevec-07-论文总结.vep [164.2M]
┃ ┃ ┣━━13.01nodevec-08-代码整体介绍.vep [236.2M]
┃ ┃ ┣━━14.01nodevec-09-代码节点和边的alias实现.vep [266.9M]
┃ ┃ ┣━━15.01nodevec-10-代码有偏随机游走和模型训练.vep [105.5M]
┃ ┃ ┣━━16.01nodevec-11-代码结果展示和总结.vep [47.1M]
┃ ┃ ┣━━17.02-line-01-论文背景..vep [102.2M]
┃ ┃ ┣━━18.02-line-02-研究成果研究意义..vep [137.7M]
┃ ┃ ┣━━19.02-line-03-前期知识..vep [70.2M]
┃ ┃ ┣━━20.02-line-04-一二阶相似度..vep [336.9M]
┃ ┃ ┣━━21.02-line-05-模型优化时间复杂度..vep [227.4M]
┃ ┃ ┣━━22.02-line-06-实验分析一..vep [323.7M]
┃ ┃ ┣━━23.02-line-07-实验分析二..vep [140.6M]
┃ ┃ ┣━━24.02-line-08-论文总结..vep [164.3M]
┃ ┃ ┣━━25.02-line-09-代码读图..vep [111.2M]
┃ ┃ ┣━━26.02-line-10-代码aliasSampling..vep [130.4M]
┃ ┃ ┣━━27.02-line-11-代码line模型实现..vep [285.5M]
┃ ┃ ┣━━28.03-sdne-01-论文背景..vep [70.1M]
┃ ┃ ┣━━29.第二次直播答疑.vep [94.8M]
┃ ┃ ┣━━30.03-sdne-02-前期知识..vep [77.9M]
┃ ┃ ┣━━31.03-sdne-03-研究成果..vep [72.6M]
┃ ┃ ┣━━32.02sdne-04-模型结构..vep [162.2M]
┃ ┃ ┣━━33.02sdne-05-一二阶相似度..vep [204.6M]
┃ ┃ ┣━━34.02sdne-06-自编码器&稀疏性问题..vep [220.5M]
┃ ┃ ┣━━35.03sdne-07-优化方法&时间复杂度..vep [271M]
┃ ┃ ┣━━36.03sdne-08-实验设置介绍..vep [312.2M]
┃ ┃ ┣━━37.03sdne-09-实验分析..vep [255.5M]
┃ ┃ ┣━━38.02sdne-10-代码模型训练..vep [187.9M]
┃ ┃ ┣━━39.03sdne-11-代码sdne模型实现..vep [178.5M]
┃ ┃ ┣━━40.03sdne-12-代码模型训练..vep [152.9M]
┃ ┃ ┣━━41.04metapath2vec-01-研究背景..vep [93.7M]
┃ ┃ ┣━━42.04metapath2vec-02-研究成果..vep [121.8M]
┃ ┃ ┣━━43.04metapath2vec-03-异质网络skip2gram..vep [172.2M]
┃ ┃ ┣━━44.04metapath2vec-04-算法细节..vep [255.4M]
┃ ┃ ┣━━45.04metapath2vec-05-实验分析..vep [320.8M]
┃ ┃ ┣━━46.04metapath2vec-06-论文总结..vep [115.6M]
┃ ┃ ┣━━47.04metapath2vec-07-代码dgl平台介绍..vep [104.2M]
┃ ┃ ┣━━48.04metapath2vec-08-代码生成meta-path训练集..vep [253.4M]
┃ ┃ ┣━━49.04metapath2vec-09-代码模型实现..vep [218.3M]
┃ ┃ ┣━━50.04metapath2vec-10-代码模型训练..vep [220M]
┃ ┃ ┣━━51.05transe-01-研究背景..vep [74.6M]
┃ ┃ ┣━━52.05transe-02-研究成果研究意义..vep [119.2M]
┃ ┃ ┣━━53.05transe-03-transE算法..vep [155.6M]
┃ ┃ ┣━━54.05transe-04-transH算法..vep [174.8M]
┃ ┃ ┣━━55.05transe-05-transR算法..vep [165.9M]
┃ ┃ ┣━━56.05transe-06-transH算法..vep [241M]
┃ ┃ ┣━━57.05transe-07-模型对比和总结..vep [53M]
┃ ┃ ┣━━58.05transe-08-实验设置和分析..vep [172.1M]
┃ ┃ ┣━━59.05transe-09-实验分析.vep.vep [123.3M]
┃ ┃ ┣━━60.05transe-10-论文总结..vep [39.9M]
┃ ┃ ┣━━61.05transe-11-代码介绍..vep [19.4M]
┃ ┃ ┣━━62.05transe-12-代码详解一..vep [156.5M]
┃ ┃ ┣━━63.05transe-13-代码详解二..vep [158.3M]
┃ ┃ ┣━━64.05transe-14-TransR等实现及代码总结..vep [187M]
┃ ┃ ┣━━65.06gat-01-研究背景..vep [72.6M]
┃ ┃ ┣━━66.06gat-02-图卷积消息传递..vep [63.1M]
┃ ┃ ┣━━67.06gat-03-研究成果研究意义..vep [77.3M]
┃ ┃ ┣━━68.06gat-04-gnn核心框架..vep [210.1M]
┃ ┃ ┣━━69.06gat-05-gat算法讲解..vep [133.9M]
┃ ┃ ┣━━70.06gat-06-各种attention总结..vep [104.8M]
┃ ┃ ┣━━71.06gat-07-multi-head起源简介..vep [53.9M]
┃ ┃ ┣━━72.06gat-08-GAT算法总结和实验设置..vep [342.9M]
┃ ┃ ┣━━73.06gat-09-论文总结..vep [128.9M]
┃ ┃ ┣━━74.06gat-10-代码介绍..vep [199.8M]
┃ ┃ ┣━━75.06gat-11-代码设置参数&读图..vep [174.4M]
┃ ┃ ┣━━76.06gat-12-邻接矩阵归一化..vep [110.6M]
┃ ┃ ┣━━77.06gat-13-gat模型实现..vep [222.1M]
┃ ┃ ┣━━78.06gat-14-gat模型训练及代码总结..vep [134.6M]
┃ ┃ ┣━━79.07graphsage-01-研究背景..vep [82.5M]
┃ ┃ ┣━━80.07graphsage-02-graphSAGE模型简介..vep [50.3M]
┃ ┃ ┣━━81.07graphsage-03-研究成果研究意义..vep [92.2M]
┃ ┃ ┣━━82.07graphsage-04-模型总览..vep [58.9M]
┃ ┃ ┣━━83.07graphsage-05-算法详解..vep [271.1M]
┃ ┃ ┣━━84.07graphsage-06-监督训练及aggregators..vep [130.5M]
┃ ┃ ┣━━85.07graphsage-07-batch训练及WLtest..vep [194.3M]
┃ ┃ ┣━━86.07graphsage-08-实验分析..vep [230.5M]
┃ ┃ ┣━━87.07graphsage-09-代码介绍.vep [127.8M]
┃ ┃ ┣━━88.07graphsage-10-读图读特征..vep [121.4M]
┃ ┃ ┣━━89.07graphsage-11-mean-aggregator讲解..vep [180.5M]
┃ ┃ ┣━━90.07graphsage-12-encoder讲解..vep [102.3M]
┃ ┃ ┣━━91.07graphsage-13-模型训练及代码总结..vep [78.3M]
┃ ┃ ┣━━92.08gcn-01-研究背景.cmproj..vep [73.7M]
┃ ┃ ┣━━93.08gcn-02-gcn模型简介..vep [63.7M]
┃ ┃ ┣━━94.08gcn-03-研究成果研究意义..vep [78.1M]
┃ ┃ ┣━━95.08gcn-04-模型总览..vep [71.8M]
┃ ┃ ┣━━96.08gcn-05-RGCN模型简介..vep [209.2M]
┃ ┃ ┣━━97.08gcn-06-拉普拉斯矩阵..vep [60.2M]
┃ ┃ ┣━━98.08gcn-07-图的频域变换..vep [63M]
┃ ┃ ┣━━99.08gcn-08-Chebyshev卷积核.vep.vep [62.5M]
┃ ┃ ┣━━100.08gcn-09-gcn频域公式推导..vep [181.7M]
┃ ┃ ┣━━101.08gcn-10-实验分析..vep [183M]
┃ ┃ ┣━━102.08gcn-11-论文总结..vep [98.9M]
┃ ┃ ┣━━103.08gcn-12-代码介绍..vep [104.4M]
┃ ┃ ┣━━104.08gcn-13-读图预处理..vep [126.6M]
┃ ┃ ┣━━105.08gcn-14-gcn模型实现及代码总结.vep.vep [111.6M]
┃ ┃ ┣━━106.09ggnn-01-研究背景..vep [94.7M]
┃ ┃ ┣━━107.09ggnn-02-ggnn模型简介..vep [58.4M]
┃ ┃ ┣━━108.09ggnn-03-研究成果研究意义..vep [73.2M]
┃ ┃ ┣━━109.09ggnn-04-模型总览..vep [153M]
┃ ┃ ┣━━110.09ggnn-05-GRU模型简单回顾..vep [50.8M]
┃ ┃ ┣━━111.09ggnn-06-GGNN模型细节..vep [175.4M]
┃ ┃ ┣━━112.09ggnn-07-GGSNNs模型细节..vep [116.9M]
┃ ┃ ┣━━113.09ggnn-08-bAbI任务..vep [225.3M]
┃ ┃ ┣━━114.09ggnn-09-RNN图数据分析..vep [77.3M]
┃ ┃ ┣━━115.09ggnn-10-实验分析&论文总结..vep [136.9M]
┃ ┃ ┣━━116.09ggnn-11-代码介绍..vep [122.5M]
┃ ┃ ┣━━117.09ggnn-12-读图..vep [273.3M]
┃ ┃ ┣━━118.09ggnn-13-ggnn模型代码..vep [429.7M]
┃ ┃ ┣━━119.09ggnn-14-模型训练和测试..vep [93.2M]
┃ ┃ ┣━━120.10mpnn-01-研究背景.vep [113.2M]
┃ ┃ ┣━━121.10mpnn-02-mpnn框架简介.vep [80.7M]
┃ ┃ ┣━━122.10mpnn-03-研究成果研究意义.vep [148.6M]
┃ ┃ ┣━━123.10mpnn-04-模型总览.vep [424.6M]
┃ ┃ ┣━━124.10mpnn-05-mpnn框架.vep [126M]
┃ ┃ ┣━━125.10mpnn-06-mpnn代表模型.vep [646.8M]
┃ ┃ ┣━━126.10mpnn-07-化学分子预测模型.vep [549.2M]
┃ ┃ ┣━━127.10mpnn-08-set2set模型.vep [349.5M]
┃ ┃ ┣━━128.10mpnn-09-专题总结.vep [126.4M]
┃ ┃ ┣━━129.10mpnn-10-实验分析.vep [416.4M]
┃ ┃ ┣━━130.10mpnn-11-论文总结.vep [222.6M]
┃ ┃ ┣━━131.10mpnn-12-代码介绍.vep [292.5M]
┃ ┃ ┣━━132.10mpnn-13-构造图.vep [556.1M]
┃ ┃ ┣━━133.10mpnn-14-DataLoader封装.vep [210.6M]
┃ ┃ ┣━━134.10mpnn-15-mpnn框架代码.vep [449.8M]
┃ ┃ ┗━━135.10mpnn-16-模型训练和测试.vep [162.1M]
┃ ┗━━02.资料 [37.7M]
┃ ┣━━01
┃ ┣━━02
┃ ┣━━03
┃ ┣━━04
┃ ┣━━05
┃ ┣━━06
┃ ┣━━07
┃ ┣━━08
┃ ┣━━09
┃ ┣━━10
┃ ┣━━论文原文
┃ ┗━━01-04代码.zip [37.7M]
┣━━6.文本匹配 [11.1G]
┃ ┣━━01.视频 [11.1G]
┃ ┃ ┣━━1.01DSSM-00专题引言.vep [34.4M]
┃ ┃ ┣━━2.01DSSM-01-学习目标..vep [9.8M]
┃ ┃ ┣━━3.01DSSM-02-论文背景、贡献及意义.vep [21.7M]
┃ ┃ ┣━━4.01DSSM-03摘要精读、总结.vep [15.8M]
┃ ┃ ┣━━5.01DSSM-04-上节回顾.vep [12.4M]
┃ ┃ ┣━━6.01DSSM-05-词哈希.vep [27.4M]
┃ ┃ ┣━━7.01DSSM-06-DSSM整体结构.vep [13M]
┃ ┃ ┣━━8.01DSSM-07-优化函数、实验与总结.vep [20.3M]
┃ ┃ ┣━━9.01DSSM-08-代码总览.vep [22.3M]
┃ ┃ ┣━━10.01DSSM-09-词哈希表的建立与数据载入.vep [47M]
┃ ┃ ┣━━11.01DSSM-10-模型的搭建与训练、测试.vep [36.9M]
┃ ┃ ┣━━12.02SiameseNet-01-孪生网络定义.vep [10.7M]
┃ ┃ ┣━━13.02SiameseNet-02-论文背景、成果、意义.vep [13.8M]
┃ ┃ ┣━━14.02SiameseNet-03-摘要带读、课程小节.vep [9.2M]
┃ ┃ ┣━━15.02SiameseNet-04-SiameseNet整体结构..vep [20.1M]
┃ ┃ ┣━━16.02SiameseNet-05-对比损失函数.vep [9.6M]
┃ ┃ ┣━━17.02SiameseNet-06-实验设置与分析.vep [12.9M]
┃ ┃ ┣━━18.02SiameseNet-07-复习、代码总览.vep [28M]
┃ ┃ ┣━━19.02SiameseNet-08-data_load..vep [34.3M]
┃ ┃ ┣━━20.02SiameseNet-09-模型搭建与训练.vep [23.1M]
┃ ┃ ┣━━21.03比较-聚合模型-01序列到序列模型..vep [28.2M]
┃ ┃ ┣━━22.03比较-聚合模型-02注意力改进的编码器解码器结构..vep [27.9M]
┃ ┃ ┣━━23.03比较-聚合模型-03文本间的注意力机制..vep [17.1M]
┃ ┃ ┣━━24.03比较-聚合模型-04论文背景及相关工作..vep [27.9M]
┃ ┃ ┣━━25.03比较-聚合模型-05论文泛读..vep [13.1M]
┃ ┃ ┣━━26.03比较-聚合模型-11SNLI数据集处理..vep [31.7M]
┃ ┃ ┣━━27.03比较-聚合模型-12数据载入模块..vep [52.8M]
┃ ┃ ┣━━28.03比较-聚合模型-13比较-聚合模型搭建与训练..vep [56.1M]
┃ ┃ ┣━━29.03比较-聚合模型-14复习、代码总览..vep [20.4M]
┃ ┃ ┣━━30.04ESIM-01学习目标与论文背景..vep [22.1M]
┃ ┃ ┣━━31.04ESIM-02论文总览与摘要带读..vep [22.1M]
┃ ┃ ┣━━32.04ESIM-03ESIM整体结构..vep [15.5M]
┃ ┃ ┣━━33.04ESIM-04输入编码层..vep [17M]
┃ ┃ ┣━━34.04ESIM-05局部推理建模层、推理组合层和输出预测层..vep [26.1M]
┃ ┃ ┣━━35.04ESIM-06实验设置与结果分析..vep [19.1M]
┃ ┃ ┣━━36.04ESIM-07论文总结与课程回顾..vep [11.4M]
┃ ┃ ┣━━37.04ESIM-08复习、代码总览..vep [19M]
┃ ┃ ┣━━38.04ESIM-09torchtext构建数据集..vep [40.5M]
┃ ┃ ┣━━39.04ESIM-10ESIM搭建与训练..vep [32.3M]
┃ ┃ ┣━━40.05BiMPM-01学习目标与研究背景..vep [13.8M]
┃ ┃ ┣━━41.05BiMPM-02相关工作..vep [12.8M]
┃ ┃ ┣━━42.05BiMPM-03研究成果、意义与论文结构..vep [8.3M]
┃ ┃ ┣━━43.05BiMPM-04摘要导读..vep [15.8M]
┃ ┃ ┣━━44.05BiMPM-05上节回顾与模型结构揣测..vep [29.5M]
┃ ┃ ┣━━45.05BiMPM-06模型整体结构..vep [9M]
┃ ┃ ┣━━46.05BiMPM-07多视角匹配..vep [19.5M]
┃ ┃ ┣━━47.05BiMPM-08实验分析与总结..vep [16.4M]
┃ ┃ ┣━━48.06RE2-01-论文研究背景.vep [143.3M]
┃ ┃ ┣━━49.06RE2-02-研究意义、摘要重点讲解.vep [83.6M]
┃ ┃ ┣━━50.06RE2-03-RE2结构讲解.vep [136.7M]
┃ ┃ ┣━━51.06RE2-04-RE2结构细节、训练技巧.vep [176.6M]
┃ ┃ ┣━━52.06RE2-05-实验设置、结果分析.vep [239.1M]
┃ ┃ ┣━━53.06RE2-06-code1.vep [311.7M]
┃ ┃ ┣━━54.06RE2-07-code2.vep [280.4M]
┃ ┃ ┣━━55.06RE2-08-code3.vep [186M]
┃ ┃ ┣━━56.07MGCN-01-泛读_研究背景、意义讲解.vep [159.2M]
┃ ┃ ┣━━57.07MGCN-02-泛读_成果、大纲介绍.vep [63.6M]
┃ ┃ ┣━━58.07MGCN-03-精读_BERT出现.vep [155.1M]
┃ ┃ ┣━━59.07MGCN-04-精读_poly-encoder.vep [192.6M]
┃ ┃ ┣━━60.07MGCN-05-精读_Bert细节.vep [240.7M]
┃ ┃ ┣━━61.07MGCN-06-code1.vep [360.7M]
┃ ┃ ┣━━62.07MGCN-07-code2.vep [437.7M]
┃ ┃ ┣━━63.07MGCN-08-code3.vep [193.9M]
┃ ┃ ┣━━64.08MatchPyramid-01-研究背景.vep [151.2M]
┃ ┃ ┣━━65.08MatchPyramid-02-论文泛读.vep [81.4M]
┃ ┃ ┣━━66.08MatchPyramid-03-算法模型总览、结构、matching matrix.vep [165.4M]
┃ ┃ ┣━━67.08MatchPyramid-04-卷积层讲解.vep [127.5M]
┃ ┃ ┣━━68.08MatchPyramid-05-matching scor.vep [147.3M]
┃ ┃ ┣━━69.08MatchPyramid-06-训练技巧、实验及总结.vep [156.7M]
┃ ┃ ┣━━70.08MatchPyramid-07-code 1.vep [197.5M]
┃ ┃ ┣━━71.08MatchPyramid-08-code 2.vep [265.4M]
┃ ┃ ┣━━72.08MatchPyramid-09-code 3.vep [264.3M]
┃ ┃ ┣━━73.09-MGCN代码讲解-08.vep [232M]
┃ ┃ ┣━━74.09-MGCN代码讲解-09.vep [369.5M]
┃ ┃ ┣━━75.09-MGCN代码讲解-10.vep [580.2M]
┃ ┃ ┣━━76.09-MGCN代码讲解-11.vep [254.4M]
┃ ┃ ┣━━77.09-MGCN论文泛读-01.vep [187.2M]
┃ ┃ ┣━━78.09-MGCN论文泛读-02.vep [90.8M]
┃ ┃ ┣━━79.09-MGCN论文精读-03.vep [187.5M]
┃ ┃ ┣━━80.09-MGCN论文精读-04.vep [132.8M]
┃ ┃ ┣━━81.09-MGCN论文精读-05.vep [155M]
┃ ┃ ┣━━82.09-MGCN论文精读-06.vep [143.9M]
┃ ┃ ┣━━83.09-MGCN论文精读-07.vep [129.8M]
┃ ┃ ┣━━84.10-01论文泛读-01.vep [175.6M]
┃ ┃ ┣━━85.10-02论文泛读-02.vep [127.5M]
┃ ┃ ┣━━86.10-03论文精读-03.vep [159.5M]
┃ ┃ ┣━━87.10-04论文精读-04.vep [143.5M]
┃ ┃ ┣━━88.10-05论文精读-05.vep [139.3M]
┃ ┃ ┣━━89.10-06论文精读-06.vep [81.6M]
┃ ┃ ┣━━90.10-07代码讲解-07.vep [202M]
┃ ┃ ┣━━91.10-08-代码讲解-08.vep [404M]
┃ ┃ ┣━━92.10-09-代码讲解-09.vep [396.2M]
┃ ┃ ┣━━93.10-10代码讲解-10.vep [501.3M]
┃ ┃ ┣━━94.10-10论文泛读-01.vep [176.1M]
┃ ┃ ┗━━95.10-11代码讲解-11.vep [391.3M]
┃ ┗━━02.资料 [0B]
┃ ┣━━01-DSSM
┃ ┣━━02-SiameseNet
┃ ┣━━03-Comp-Agg
┃ ┣━━04-ESIM
┃ ┣━━05-BIMPM
┃ ┣━━06-RE2
┃ ┣━━07 MGCN
┃ ┣━━08 BERTPoly-encoders
┃ ┣━━09 MGCN
┃ ┗━━10 SemBERT
┣━━7.机器翻译 [6.1G]
┃ ┣━━00.资料 [0B]
┃ ┃ ┗━━学员代码课件
┃ ┗━━01.视频 [6.1G]
┃ ┣━━1.1-loung_nmt-储备知识.vep [76.8M]
┃ ┣━━1.10-loung_nmt_训练和测试.vep [273.9M]
┃ ┣━━1.2-loung_nmt-研究背景.vep [275.7M]
┃ ┣━━1.3-loung_nmt-研究成果及意义.vep [49.4M]
┃ ┣━━1.4-luong_nmt-论文简介.vep [236.8M]
┃ ┣━━1.5-luong_nmt-global_attention.vep [329.1M]
┃ ┣━━1.6-luong_nmt-local_attention.vep [257.8M]
┃ ┣━━1.7-luong_nmt_2_4_实验.vep [369.8M]
┃ ┣━━1.8-loung_nmt_数据读取.vep [243.2M]
┃ ┣━━1.9-loung_nmt_模型实现.vep [567.4M]
┃ ┣━━2.1-coverage_储备知识.vep [59.1M]
┃ ┣━━2.2-coverage_研究背景及意义.vep [138.1M]
┃ ┣━━2.3-coverage_相关知识.vep [196.8M]
┃ ┣━━2.4-coverage_基于语言学的覆盖模型.vep [207.1M]
┃ ┣━━2.5-coverage_基于神经网络的覆盖模型.vep [191M]
┃ ┣━━2.6-coverage 代码实践.vep [534.5M]
┃ ┣━━3.1-subword_nmt_1_1.vep [94.6M]
┃ ┣━━3.2-subword_nmt.vep [194.6M]
┃ ┣━━3.3-subword_nmt_.vep [480.5M]
┃ ┣━━3.4-subword_nmt_.vep [335.8M]
┃ ┣━━3.5-subword_nmt_.vep [606.9M]
┃ ┗━━3.6-subword_nmt.vep [505.6M]
┣━━8.情感分析 [7.8G]
┃ ┣━━01.视频 [7.6G]
┃ ┃ ┣━━1.第一篇论文背景+精读部分.vep [459.4M]
┃ ┃ ┣━━2.第一篇代码讲解部分.vep [307.1M]
┃ ┃ ┣━━3.1-1论文导读..vep [96.3M]
┃ ┃ ┣━━4.1-2研究背景..vep [182M]
┃ ┃ ┣━━5.1-3论文泛读..vep [129.7M]
┃ ┃ ┣━━6.2-1上节课回顾..vep [15.6M]
┃ ┃ ┣━━7.2-2论文精读-模型结构..vep [138M]
┃ ┃ ┣━━8.2-3算法细节一..vep [111.2M]
┃ ┃ ┣━━9.2-4算法细节二..vep [107.1M]
┃ ┃ ┣━━10.2-5实验设置与分析..vep [359.4M]
┃ ┃ ┣━━11.2-6论文总结..vep [40.9M]
┃ ┃ ┣━━12.2-7本课回顾及下节预告..vep [22.4M]
┃ ┃ ┣━━13.3-1代码介绍..vep [124M]
┃ ┃ ┣━━14.3-2代码讲解一..vep [407.7M]
┃ ┃ ┣━━15.3-3代码讲解二..vep [392.1M]
┃ ┃ ┣━━16.3-4代码讲解三..vep [192.4M]
┃ ┃ ┣━━17.1-1论文导读..vep [17.7M]
┃ ┃ ┣━━18.1-2 知识储备..vep [23.4M]
┃ ┃ ┣━━19.1-3学习目标..vep [14.3M]
┃ ┃ ┣━━20.1-4 课程安排..vep [8.8M]
┃ ┃ ┣━━21.1-5 研究背景..vep [134.9M]
┃ ┃ ┣━━22.1-6 论文泛读..vep [74.3M]
┃ ┃ ┣━━23.1-7 下节预告..vep [12.5M]
┃ ┃ ┣━━24.2-1 上节回顾..vep [13.6M]
┃ ┃ ┣━━25.2-2 论文精读..vep [69.9M]
┃ ┃ ┣━━26.2-3 论文精读TD-LSTM..vep [153.1M]
┃ ┃ ┣━━27.2-4 论文精读ATAE-LSTM..vep [239.7M]
┃ ┃ ┣━━28.2-5 实验结果及分析一.vep [188.9M]
┃ ┃ ┣━━29.2-6 实验结果及分析二..vep [73.4M]
┃ ┃ ┣━━30.2-7 论文总结及下节回顾..vep [59.1M]
┃ ┃ ┣━━31.3-1 代码介绍..vep [459.8M]
┃ ┃ ┣━━32.3-2 代码讲解二.vep [421.8M]
┃ ┃ ┣━━33.3-3代码讲解三..vep [256.5M]
┃ ┃ ┣━━34.3-4 代码讲解回顾..vep [31.8M]
┃ ┃ ┣━━35.1-1 论文介绍..vep [33M]
┃ ┃ ┣━━36.1-2 研究背景..vep [85M]
┃ ┃ ┣━━37.1-3 论文泛读..vep [22.1M]
┃ ┃ ┣━━38.1-4 本课回顾与下节预告..vep [13.1M]
┃ ┃ ┣━━39.2-1 上节回顾..vep [7.1M]
┃ ┃ ┣━━40.2-2 论文精读..vep [116M]
┃ ┃ ┣━━41.2-3 实验设置及分析..vep [91.5M]
┃ ┃ ┣━━42.2-4 论文总结及回顾..vep [27.6M]
┃ ┃ ┣━━43.3-1 代码环境讲解..vep [103.1M]
┃ ┃ ┣━━44.3-2 代码结构讲解..vep [392.1M]
┃ ┃ ┣━━45.3-3 论文代码细节讲解..vep [352.4M]
┃ ┃ ┣━━46.3-4 代码实践课回顾..vep [45.5M]
┃ ┃ ┣━━47.1-1 论文介绍..vep [49.3M]
┃ ┃ ┣━━48.1-2 背景介绍1..vep [65.3M]
┃ ┃ ┣━━49.1-3 背景介绍2..vep [48.8M]
┃ ┃ ┣━━50.1-4 论文泛读..vep [14.9M]
┃ ┃ ┣━━51.2-1 上节回顾..vep [9.1M]
┃ ┃ ┣━━52.2-2 论文算法总览..vep [120M]
┃ ┃ ┣━━53.2-3 论文精读..vep [29.7M]
┃ ┃ ┣━━54.2-4 模型Fine-tuning..vep [111.2M]
┃ ┃ ┣━━55.2-5 实验设置及分析..vep [46.6M]
┃ ┃ ┣━━56.2-6 论文总结..vep [25.8M]
┃ ┃ ┣━━57.2-7 论文回顾..vep [17.6M]
┃ ┃ ┣━━58.3-1 实践代码介绍..vep [144.8M]
┃ ┃ ┣━━59.3-2 实践代码精讲1..vep [409.2M]
┃ ┃ ┗━━60.3-3 实践代码精讲2..vep [82.4M]
┃ ┗━━02.资料 [138.4M]
┃ ┣━━01-TextRNN 代码.zip [27.4M]
┃ ┣━━Paper02_TreeLSTM.zip [19.6M]
┃ ┣━━Paper03_TD-LSTM_TC-LSTM_AT-LSTM.zip [11.4M]
┃ ┣━━Paper04_MemNet.zip [18.8M]
┃ ┗━━Paper05_BERT-ABSA.zip [61.1M]
┣━━9.阅读理解 [9.9G]
┃ ┣━━01.视频 [9.9G]
┃ ┃ ┣━━1.NLP-MRC-背景知识.vep [117.9M]
┃ ┃ ┣━━2.NLP-MRC-论文精读.vep [206.4M]
┃ ┃ ┣━━3.code1-1..vep [264.2M]
┃ ┃ ┣━━4.code1-2..vep [457.1M]
┃ ┃ ┣━━5.feedback.vep [98.6M]
┃ ┃ ┣━━6.02-bidaf_1_1_背景意义..vep [82.4M]
┃ ┃ ┣━━7.02-bidaf_1_2_相关工作+小结..vep [67.4M]
┃ ┃ ┣━━8.02-bidaf_2_1_模型结构..vep [74.6M]
┃ ┃ ┣━━9.02-bidaf_2_2_实验分析..vep [49.3M]
┃ ┃ ┣━━10.02-bidaf_3_1_数据读取-jupyter..vep [176.6M]
┃ ┃ ┣━━11.02-bidaf_3_2数据读取-pycharm..vep [245.4M]
┃ ┃ ┣━━12.02-bidaf_4_训练加预测..vep [305.1M]
┃ ┃ ┣━━13.02-bidaf_5_评测指标..vep [126M]
┃ ┃ ┣━━14.02-bidaf_6_反馈..vep [98.6M]
┃ ┃ ┣━━15.03-pgnet_1_1_研究背景..vep [105.7M]
┃ ┃ ┣━━16.03-pgnet_1_2_研究背景意义第二部分..vep [44.2M]
┃ ┃ ┣━━17.03-pgnet_2_1_模型部分..vep [118.8M]
┃ ┃ ┣━━18.03-pgnet_2_2_实验+前沿论文（上）..vep [145.2M]
┃ ┃ ┣━━19.03-pgnet_2_3_前沿论文(下)..vep [166.9M]
┃ ┃ ┣━━20.03-pgnet_2_4_模型总结..vep [22.9M]
┃ ┃ ┣━━21.03-pgnet_3_code-review..vep [85.5M]
┃ ┃ ┣━━22.03-pgnet_4_1_数据处理第一部分..vep [420.9M]
┃ ┃ ┣━━23.03-pgnet_4_2_数据处理第二部分..vep [85.9M]
┃ ┃ ┣━━24.03-pgnet_5_1_train第一部分..vep [68.9M]
┃ ┃ ┣━━25.03-pgnet_5_2_train第二部分..vep [351.6M]
┃ ┃ ┣━━26.03-pgnet_6_1_预测第一部分..vep [271.7M]
┃ ┃ ┣━━27.03-pgnet_6_2_预测第二部分..vep [122.1M]
┃ ┃ ┣━━28.04-adv_1_1_研究背景.vep [150.6M]
┃ ┃ ┣━━29.04-adv_1_2_研究成果和小节.vep [87.7M]
┃ ┃ ┣━━30.04-adv_2_1_模型和实验.vep [266.5M]
┃ ┃ ┣━━31.04-adv_2_2_2020智能技术竞赛介绍.vep [208.9M]
┃ ┃ ┣━━32.04-adv_3_1_code-overview第一部分.vep [265.4M]
┃ ┃ ┣━━33.04-adv_3_2_code-overview第二部分.vep [202M]
┃ ┃ ┣━━34.04-adv_4_数据处理.vep [411.7M]
┃ ┃ ┣━━35.04-adv_5_1_train-第一部分.vep [354.8M]
┃ ┃ ┣━━36.04-adv_5_2_train第二部分.vep [420.1M]
┃ ┃ ┣━━37.04-adv_6_预测部分.vep [339.7M]
┃ ┃ ┣━━38.05-xlnet_1_1_研究背景第一部分.vep [146.9M]
┃ ┃ ┣━━39.05-xlnet_1_2_研究背景第二部分.vep [176.8M]
┃ ┃ ┣━━40.05-xlnet_2_1_论文模型第一部分.vep [279M]
┃ ┃ ┣━━41.05-xlnet_2_2_论文模型第二部分.vep [151M]
┃ ┃ ┣━━42.05-xlnet_3_代码overview.vep [222.7M]
┃ ┃ ┣━━43.05-xlnet_4_数据处理overview.vep [76.4M]
┃ ┃ ┣━━44.05-xlnet_5_1_数据处理第一部分.vep [367.9M]
┃ ┃ ┣━━45.05-xlnet_5_2_数据处理第二部分.vep [497.4M]
┃ ┃ ┣━━46.05-xlnet_6_1_训练代码第一部分.vep [390.4M]
┃ ┃ ┣━━47.05-xlnet_6_2_训练代码第二部分.vep [258.1M]
┃ ┃ ┣━━48.05-xlnet_7_1_预测第一部分.vep [287.3M]
┃ ┃ ┣━━49.05-xlnet_7_2_预测第二部分.vep [187.1M]
┃ ┃ ┗━━50.专题总结.vep [28.3M]
┃ ┗━━02.资料 [0B]
┃ ┣━━第二课
┃ ┣━━第三课
┃ ┣━━第四课
┃ ┣━━第五课
┃ ┗━━第一课
┣━━10.【新专题】对话系统 [15.6G]
┃ ┣━━00.资料 [0B]
┃ ┃ ┣━━01 joint-bert 资料
┃ ┃ ┣━━02
┃ ┃ ┣━━03
┃ ┃ ┗━━04
┃ ┗━━01.视频 [15.6G]
┃ ┣━━【10月13日】Fewshot MultiLABEL-论文精读（第二部分）.vep [806.2M]
┃ ┣━━【10月23日】Fewshot MultiLABEL-论文精读+代码讲解.vep [1.6G]
┃ ┣━━【10月27日】Fewshot MultiLABEL-代码复现.vep [656.4M]
┃ ┣━━【8月10日】对话系统体验课直播第一讲.vep [398.7M]
┃ ┣━━【8月11日】对话系统体验课直播第二讲.vep [442.5M]
┃ ┣━━【8月19日】JointBERT-论文讲解（开营直播）.vep [1.4G]
┃ ┣━━【8月21日】JointBERT-代码详解.vep [1.8G]
┃ ┣━━【8月25日】AGIF-论文讲解.vep [922.7M]
┃ ┣━━【8月28日】AGIF-论文精读.vep [2G]
┃ ┣━━【9月11日】AGIF-代码复现.vep [1.8G]
┃ ┣━━【9月16日】Fewshot MultiLABEL-论文泛读.vep [979.1M]
┃ ┣━━【9月19日】Fewshot MultiLABEL-论文精读（第一部分）.vep [802.9M]
┃ ┣━━01 joint-bert-1.vep [234M]
┃ ┣━━01 joint-bert-2.vep [50.1M]
┃ ┣━━01 joint-bert-3.vep [29M]
┃ ┣━━01 joint-bert-4.vep [563.3M]
┃ ┣━━01 joint-bert-5.vep [43.6M]
┃ ┣━━01 joint-bert-6.vep [5.8M]
┃ ┣━━01 joint-bert-7.vep [200.8M]
┃ ┣━━01 joint-bert-8-代码.vep [186.9M]
┃ ┗━━01 joint-bert-9-代码.vep [909.6M]
┣━━前沿直播 [5.7G]
┃ ┣━━01.视频 [5.7G]
┃ ┃ ┣━━01-第一场 基于图神经网络的应用和开发——关系抽取.vep [412.3M]
┃ ┃ ┣━━02-第二场—预训练模型RoBERTa.vep [472.5M]
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┃ ┃ ┣━━05-第五场-COMET.vep [640.8M]
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┃ ┃ ┗━━12-第十二场直播——如何快速发论文.vep [441.1M]
┃ ┗━━00.资料.zip [42.8M]
┗━━vep加密播放说明.txt [204B]

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