208

.gitignore

@@ -15,7 +15,7 @@ model/
 version/
 **/version/
 **/gitinfo/
-**/mywork/
+**/mywork/*
 
 T1_Relation_Classification_via_CDNN/saved_models/*
 

README.md

@@ -45,15 +45,10 @@
         - [【关于 NLP分类任务】那些你不知道的事](#关于-nlp分类任务那些你不知道的事)
       - [实战篇](#实战篇)
         - [重点推荐篇](#重点推荐篇)
-        - [推荐篇](#推荐篇)
-      - [视频学习篇](#视频学习篇)
     - [Elastrsearch 学习篇](#elastrsearch-学习篇)
     - [推荐系统 学习篇](#推荐系统-学习篇)
     - [竞赛篇](#竞赛篇)
     - [GCN_study学习篇](#gcn_study学习篇)
-    - [ML 小白入门篇](#ml-小白入门篇)
-    - [资源篇](#资源篇)
-    - [CV 入门 实战篇](#cv-入门-实战篇)
 
 ## 介绍
 
@@ -954,102 +949,6 @@
   - [【关于 Bert 源码解析IV 之 句向量生成篇 】 那些的你不知道的事](https://github.com/km1994/nlp_paper_study/blob/master/bert_study/T1_bert/bertCode4_word2embedding.md) 
   - [【关于 Bert 源码解析V 之 文本相似度篇 】 那些的你不知道的事](https://github.com/km1994/nlp_paper_study/blob/master/bert_study/T1_bert/bertCode5_similarity.md)
 
-##### 推荐篇
-
-- [爬虫 实战篇](https://github.com/km1994/Conversation_Wp/tree/master/t11_scrapyWp)
-  - [Scrapy 爬虫 实战篇](https://github.com/km1994/Conversation_Wp/tree/master/t11_scrapyWp):主要介绍使用 scrapy 构建网络爬虫，并爬去百度搜索引擎数据
-
-- [特征提取 实战篇](https://github.com/km1994/text_feature_extraction)
-  - [关键词提取、关键短语提取、文本摘要提取 实战篇](https://github.com/km1994/text_feature_extraction)
-  - [TF-idf 特征提取 实战篇](https://github.com/km1994/text_feature_extraction)
-  - [pynlp 关键词提取 实战篇](https://github.com/km1994/Conversation_Wp/tree/master/t10_pynlpirWp)
-
-- [词向量预训练 实战篇](https://github.com/km1994/TextClassifier/tree/master/word2vec_train)
-  - [word2vec 词向量预训练 实战篇](https://github.com/km1994/TextClassifier/tree/master/word2vec_train)
-  - [fasttext 词向量预训练 实战篇](https://github.com/km1994/TextClassifier/tree/master/word2vec_train)
-
-- [中文情感分析 实战篇](https://github.com/km1994/sentiment_analysis)
-  - [word2vec](https://github.com/km1994/sentiment_analysis/tree/master/word2vec)
-  - [textCNN](https://github.com/km1994/sentiment_analysis/tree/master/textCNN)
-  - [charCNN](https://github.com/km1994/sentiment_analysis/tree/master/charCNN)
-  - [RCNN](https://github.com/km1994/sentiment_analysis/tree/master/RCNN)
-  - [Bi-LSTM](https://github.com/km1994/sentiment_analysis/tree/master/Bi-LSTM)
-  - [Bi-LSTM+Attention](https://github.com/km1994/sentiment_analysis/tree/master/Bi-LSTM%2BAttention)
-  - [adversarialLSTM](https://github.com/km1994/sentiment_analysis/tree/master/adversarialLSTM)
-  - [Transformer](https://github.com/km1994/sentiment_analysis/tree/master/Transformer)
-  - [ELMo](https://github.com/km1994/sentiment_analysis/tree/master/ELMo)
-  - [BERT](https://github.com/km1994/sentiment_analysis/tree/master/BERT)
-
-- [中文文本分类 实战篇](https://github.com/km1994/TextClassifier)
-  - [Tensorflow 篇](https://github.com/km1994/TextClassifier)
-    - [FastText](https://github.com/km1994/TextClassifier/tree/master/fastTextStudy.ipynb)
-    - [TextCNN](https://github.com/km1994/TextClassifier/tree/master/dl_model)
-    - [TextRNN](https://github.com/km1994/TextClassifier/tree/master/dl_model)
-    - [TextRCNN](https://github.com/km1994/TextClassifier/tree/master/dl_model)
-    - [BiLSTMAttention](https://github.com/km1994/TextClassifier/tree/master/dl_model)
-    - [AdversarialLSTM](https://github.com/km1994/TextClassifier/tree/master/dl_model)
-    - [Transformer](https://github.com/km1994/TextClassifier/tree/master/dl_model)
-  - [pytorch 篇](https://github.com/km1994/Chinese-Text-Classification-Pytorch)
-    - [FastText](https://github.com/km1994/Chinese-Text-Classification-Pytorch)
-    - [TextCNN](https://github.com/km1994/Chinese-Text-Classification-Pytorch)
-    - [TextRNN](https://github.com/km1994/Chinese-Text-Classification-Pytorch)
-    - [TextRCNN](https://github.com/km1994/Chinese-Text-Classification-Pytorch)
-    - [BiLSTMAttention](https://github.com/km1994/Chinese-Text-Classification-Pytorch)
-    - [DPCNN](https://github.com/km1994/Chinese-Text-Classification-Pytorch)
-    - [AdversarialLSTM](https://github.com/km1994/Chinese-Text-Classification-Pytorch)
-    - [Transformer](https://github.com/km1994/Chinese-Text-Classification-Pytorch)
-
-- [命名实体识别 “史诗级” 入门教程](https://github.com/km1994/NERer)
-  - [HMM 做命名实体识别](https://github.com/km1994/named_entity_recognition/models/hmm.py)
-  - [CRF 做命名实体识别](https://github.com/km1994/named_entity_recognition/models/crf.py)
-  - [BiLSTM-CRF 做命名实体识别](https://github.com/km1994/NERer/tree/master/LSTM_IDCNN)
-  - [IDCNN-CRF 做命名实体识别](https://github.com/km1994/NERer/tree/master/LSTM_IDCNN)
-  - [BERT-CRF 做命名实体识别](https://github.com/km1994/NERer/tree/master/bert_crf)
-  - [ALBERT-CRF 做命名实体识别](https://github.com/km1994/NERer/tree/master/albert_crf)
-
-- [知识图谱 实战篇]()
-  - [KBQA-BERT](https://github.com/km1994/KBQA-BERT)
-
-- [问答系统 实战篇](https://github.com/km1994/Conversation_Wp/tree/master/baidu_qa_zh_process)
-  - [基于 百度问答 的问答系统](https://github.com/km1994/Conversation_Wp/tree/master/baidu_qa_zh_process/Baidu_WebQA_model.ipynb)
-
-- [文本匹配 实战篇](https://github.com/km1994/TextMatching)
-  - [TextMatching](https://github.com/km1994/TextMatching)
-  - [TextMatch](https://github.com/km1994/TextMatch)
-  - [Text_Matching（文本匹配算法）](https://github.com/km1994/Text_Matching)
-
-- [预训练模型 实战篇]()
-  - [bert](https://github.com/km1994/bert)
-  - [Chinese-PreTrained-XLNet](https://github.com/km1994/Chinese-PreTrained-XLNet)
-
-- [模型蒸馏 实战篇]()
-  - [基于BERT的蒸馏实验](https://github.com/km1994/bert_distill)
-
-#### 视频学习篇
-
-- [CS224n 视频学习篇](https://github.com/km1994/Datawhale_NLP_CS224n)
-  -  [Lecture 1: Introduction and Word Vectors](https://github.com/km1994/Datawhale_NLP_CS224n/tree/master/Lecture/Lecture1)
-  -  [Lecture 2: Word Vectors and Word Senses](https://github.com/km1994/Datawhale_NLP_CS224n/tree/master/Lecture/Lecture2)
-  -  [Lecture 3: Word Window Classification, Neural Networks, and Matrix Calculus](https://github.com/km1994/Datawhale_NLP_CS224n/tree/master/Lecture/Lecture3)
-  -  [Lecture 4: Backpropagation](https://github.com/km1994/Datawhale_NLP_CS224n/tree/master/Lecture/Lecture4)
-  -  [Lecture 5: Dependency Parsing](https://github.com/km1994/Datawhale_NLP_CS224n/tree/master/Lecture/Lecture5)
-  -  [Lecture 6: Language Models and RNNs](https://github.com/km1994/Datawhale_NLP_CS224n/tree/master/Lecture/Lecture6)
-  -  [Lecture 7: Vanishing Gradients, Fancy RNNs](https://github.com/km1994/Datawhale_NLP_CS224n/tree/master/Lecture/Lecture7)
-  -  [Lecture 8: Translation, Seq2Seq, Attention](https://github.com/km1994/Datawhale_NLP_CS224n/tree/master/Lecture/Lecture8)
-  -  [Lecture 9: Practical Tips for Projects](https://github.com/km1994/Datawhale_NLP_CS224n/tree/master/Lecture/Lecture9)
-  -  [Lecture 10: Question Answering](https://github.com/km1994/Datawhale_NLP_CS224n/tree/master/Lecture/Lecture10)
-  -  [Lecture 11: Convolutional Networks for NLP](https://github.com/km1994/Datawhale_NLP_CS224n/tree/master/Lecture/Lecture11)
-  -  [Lecture 12: Subword Models](https://github.com/km1994/Datawhale_NLP_CS224n/tree/master/Lecture/Lecture12)
-  -  [Lecture 13: Contextual Word Embeddings](https://github.com/km1994/Datawhale_NLP_CS224n/tree/master/Lecture/Lecture13)
-  -  [Lecture 14: Transformers and Self-Attention](https://github.com/km1994/Datawhale_NLP_CS224n/tree/master/Lecture/Lecture14)
-  -  [Lecture 15: Natural Language Generation](https://github.com/km1994/Datawhale_NLP_CS224n/tree/master/Lecture/Lecture15)
-  -  [Lecture 16: Coreference Resolution](https://github.com/km1994/Datawhale_NLP_CS224n/tree/master/Lecture/Lecture16)
-  -  [Lecture 17: Multitask Learning](https://github.com/km1994/Datawhale_NLP_CS224n/tree/master/Lecture/Lecture17)
-  -  [Lecture 18: Constituency Parsing, TreeRNNs](https://github.com/km1994/Datawhale_NLP_CS224n/tree/master/Lecture/Lecture18)
-  -  [Lecture 19: Bias in AI](https://github.com/km1994/Datawhale_NLP_CS224n/tree/master/Lecture/Lecture19)
-  -  [Lecture 20: Future of NLP + Deep Learning](https://github.com/km1994/Datawhale_NLP_CS224n/tree/master/Lecture/Lecture20)
-
-
 ### Elastrsearch 学习篇
 
 - [Elastrsearch 学习](es_study/)
@@ -1102,60 +1001,4 @@
   - [DGL 入门](https://github.com/km1994/GCN_study/blob/master/DGL_study/DGL_introduction.md)
   - [DGL 入门 —— GCN 实现](https://github.com/km1994/GCN_study/blob/master/DGL_study/DGL_GCN_introduction.md)
 
-### [ML 小白入门篇](https://github.com/km1994/ML_beginer)
-
-- [【关于 Logistic Regression 】那些你不知道的事](https://github.com/km1994/nlp_paper_study/tree/master/ML_study/LogisticRegression_study/)
-- [概率图模型串烧 （HMM->MEMM->CRF）](https://github.com/km1994/ML_beginer/tree/master/CRF_study)
-
-- [KNN 算法 学习篇](https://github.com/km1994/MLStudy#%E4%B8%80knnstudy)
-  - [理论篇](https://mp.weixin.qq.com/s?__biz=MzAxMTU5Njg4NQ==&mid=100000006&idx=7&sn=f040e0a95376880349378ce7afd634af&chksm=1bbfe43c2cc86d2a5942e0938c871375cffcd72c9795a3e8b39fdb0c8285348631e7bf8a27b4)
-  - [实战篇](https://github.com/km1994/MLStudy/blob/master/KNNStudy/KNNClass.py)
-
-- [朴素贝叶斯算法 学习篇](https://github.com/km1994/MLStudy#%E4%BA%8Cnbstudy)
-  - [NB 算法理论学习](https://github.com/km1994/MLStudy/blob/master)
-  - [NB 算法实现](https://github.com/km1994/MLStudy/blob/master/NBStudy/NBStudy.py)
-
-- [Apriori 算法 学习篇](https://github.com/km1994/MLStudy#%E4%B8%89aprioristudy)
-  - [Apriori 算法理论学习](https://github.com/km1994/MLStudy/blob/master)
-  - [Apriori 算法实现](https://github.com/km1994/MLStudy/blob/master/AprioriStudy/AprioriMyTest.py)
-
-- [Softmax 算法学习篇](https://github.com/km1994/MLStudy#%E5%8D%81softmax-numpy-%E5%AE%9E%E7%8E%B0)
-  - [Softmax 理论学习](https://mp.weixin.qq.com/s?__biz=MzAxMTU5Njg4NQ==&mid=100001925&idx=5&sn=20c5ead4f4b5f8f88c30043fb3703557&chksm=1bbfedbf2cc864a96b5fc4575e09294478f6f4cff65d654b8d775fed78766f80faf333d8ca08&scene=20&xtrack=1#rd)
-  - [Softmax 算法实现](https://github.com/km1994/MLStudy/blob/master/softmaxStudy/softmaxStudy.py)
-
-- [Gradient Descent 算法学习篇](https://github.com/km1994/MLStudy#%E5%9B%9Bgradientdescentstudy)
-  - [GradientDescent 算法理论学习](https://github.com/km1994/MLStudy/blob/master)
-  - [GradientDescent 算法实现](https://github.com/km1994/MLStudy/blob/master/GradientDescentStudy/GradientDescentTest.py)
-
-- [随机森林算法 学习篇](https://github.com/km1994/MLStudy#%E4%BA%94randomforest)
-  - [RandomForest 算法理论学习](https://mp.weixin.qq.com/s?__biz=MzAxMTU5Njg4NQ==&mid=100001388&idx=1&sn=21bae727bf3510fad98b3ec4a89d124e&chksm=1bbfe3562cc86a40769ea726f96e3a45185697f9582a2e3fbbbeec3af90dd722ebe09b635ddc&scene=20&xtrack=1#rd)
-  - [RandomForest 算法实现](https://github.com/km1994/MLStudy/blob/master/RandomForest/xiechengRF/RandomForestClass.py)
-  - [基于PCA 的 RandomForest 算法实现](https://github.com/km1994/MLStudy/blob/master/RandomForest/xiechengRF/xiechengPCARF.py)
-
-- [EM 算法学习篇](https://github.com/km1994/ML_beginer/tree/master/EM_study)
-
-- [SVM 算法学习篇](https://github.com/km1994/MLStudy#%E5%85%ADsvn)
-  - [SVN 算法理论学习](https://mp.weixin.qq.com/s?__biz=MzAxMTU5Njg4NQ==&mid=100001388&idx=1&sn=21bae727bf3510fad98b3ec4a89d124e&chksm=1bbfe3562cc86a40769ea726f96e3a45185697f9582a2e3fbbbeec3af90dd722ebe09b635ddc&scene=20&xtrack=1#rd)
-  - [SVM 算法学习篇](https://github.com/km1994/ML_beginer/tree/master/SVM_study)
-  - [SVN 算法实现](https://github.com/km1994/MLStudy/blob/master/SVN/PCAandSVN.py)
-
-- [BPNN 算法 学习篇](https://github.com/km1994/MLStudy#%E4%B8%83bpnn)
-  - [BPNN 算法理论学习](https://github.com/km1994/MLStudy/blob/master)
-  - [BPNN 算法实现](https://github.com/km1994/MLStudy/blob/master/ANN/BP/test.py)
-
-- [PCA 算法 学习篇](https://github.com/km1994/MLStudy#%E5%85%ABpca)
-  - [PCA 算法理论学习](https://github.com/km1994/MLStudy/blob/master)
-  - [PCA 算法实现](https://github.com/km1994/MLStudy/blob/master/DimensionalityReduction/PCA/PCAClass.py)
-
-- [CNN 算法 学习篇](https://github.com/km1994/MLStudy#%E4%B9%9Dcnn-numpy-%E5%AE%9E%E7%8E%B0)
-  - [卷积运算的定义、动机](https://mp.weixin.qq.com/s?__biz=MzAxMTU5Njg4NQ==&mid=100000973&idx=2&sn=4eced9e7204274a4e3798cb73a140c72&chksm=1bbfe1f72cc868e1f63262fad39b4f735a6d424c064f6bee755e438b94487bf75b5d41cc02c0&scene=20&xtrack=1&key=fe048f5ad4fa1bcff1ed72e320faab18cb01c02c1a16279c60775734b428e42206e9f5a8f3c402ae96c01259df639ca04206da43e2ab1b42bfaf44bb4068c793df27faa893ea0301a375086e4adfd3b7&ascene=1&uin=MjQ3NDIwNTMxNw%3D%3D&devicetype=Windows+10&version=62060426&lang=zh_CN&pass_ticket=906xy%2Fk9TQJp5jnyekYc89wLa17ODmZRkas9HXdX%2BtYcy0q32NIxLHOhFx519Yxa)
-  - [反卷积Deconvolution](https://mp.weixin.qq.com/s?__biz=MzAxMTU5Njg4NQ==&mid=100000973&idx=3&sn=8a787cc0e165fa071ca7a602f16fae17&chksm=1bbfe1f72cc868e1249a3ebe90021e2a6e12c3d8021fcc1877a5390eed36f5a8a6698eb65216&scene=20&xtrack=1#rd)
-  - [池化运算的定义](https://mp.weixin.qq.com/s?__biz=MzAxMTU5Njg4NQ==&mid=100000973&idx=4&sn=cebf71790dd7e0e497fa36fa199c368d&chksm=1bbfe1f72cc868e1017d26a996f1eb7602fad1efced62713def3012b8df1b85b6ba46c0ebae8&scene=20&xtrack=1#rd)
-  - [CNN 算法 numpy 实现](https://github.com/km1994/MLStudy/blob/master/CNN-Numpy_suss/view.py)
-
-### [资源篇](https://github.com/km1994/funNLP)
-- [funNLP](https://github.com/km1994/funNLP)
-
-### [CV 入门 实战篇](https://github.com/km1994/cv_entrance)
-