本课程内容完整,不仅通过增加实战项目数量横向提升课程内容的丰富度，同时还充实了每一个项目的内容和数据来纵向扩充课程内容，实战和理论相结合,确保课程项目紧随企业内部业务发展的步伐和当下最新最前沿的技术热点。京东智联云联合贪心科技推出的人工智能课程“NLP实战训练营”首期上月底圆满结束，这是行业第一个全程依托于企业真实项目授课的实战训练营。经过半年多的直播线上教学和实战指导，首期实训练营共培养了168名有志于从事AI领域的高级人才，为企业输送了大量自然语言理解方向的精英人才。

【课程介绍】：

比2020往期课程新增一个项目,其他内容均有所提升

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