贪心-京东NLP线上实习营课程介绍:
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”首期上月底圆满成功，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，持续开展的实训练营吸纳了全球优质开发者人员，为京东智联云打造了优质的开发者社群，储备高端人才库，为企业输送了大量自然语言理解方向的精英人才。同时也直接促进了京东AI开发平台的完善，持续带动GPU云资源消耗，提升京东智联云在AI领域的影响力。
课程目录:
┣━━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]
