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BI4O's Projects

ml_git_repos icon ml_git_repos

使用notebook进行机器学习、深度学习实战的代码

models icon models

Pre-trained and Reproduced Deep Learning Models （『飞桨』官方模型库，包含多种学术前沿和工业场景验证有效的领先模型）

modin icon modin

Modin: Speed up your Pandas workflows by changing a single line of code

nlp icon nlp

learn about RNN, LSTM

nlp-journey icon nlp-journey

nlp相关的一些论文及代码, 包括词向量(Word Embedding)、命名实体识别(NER)、文本分类(Text Classificatin)、文本生成、文本相似性(Text Similarity)计算等，基于keras和tensorflow

nlp-tutorial icon nlp-tutorial

Natural Language Processing Tutorial for Deep Learning Researchers

pinyin2chinese icon pinyin2chinese

Self complemented Pinyin2Chinese demo use algorithms including Trie and HMM model , 基于隐马尔科夫模型与Trie树的拼音切分与拼音转中文的简单demo实现。

pinyin2hanzi icon pinyin2hanzi

拼音转汉字， 拼音输入法引擎， pin yin -> 拼音

pumpkin-book icon pumpkin-book

《机器学习》（西瓜书）公式推导解析，在线阅读地址：https://datawhalechina.github.io/pumpkin-book

python-uiautomation-for-windows icon python-uiautomation-for-windows

(Donot use 3.7.6,3.8.1):snake:Python 3 wrapper of Microsoft UIAutomation. Support UIAutomation for MFC, WindowsForm, WPF, Modern UI(Metro UI), Qt, IE, Firefox, Chrome ...

pytorch icon pytorch

Tensors and Dynamic neural networks in Python with strong GPU acceleration

pytorch-handbook icon pytorch-handbook

pytorch handbook是一本开源的书籍，目标是帮助那些希望和使用PyTorch进行深度学习开发和研究的朋友快速入门，其中包含的Pytorch教程全部通过测试保证可以成功运行

qasystemonmedicalkg icon qasystemonmedicalkg

A tutorial and implement of disease centered Medical knowledge graph and qa system based on it。知识图谱构建，自动问答，基于kg的自动问答。以疾病为中心的一定规模医药领域知识图谱，并以该知识图谱完成自动问答与分析服务。

rasa_milktea_chatbot icon rasa_milktea_chatbot

Chatbot with bert chinese model, base on rasa framework（中文聊天机器人，结合bert意图分析，基于rasa框架）

rasa_nlu_chi icon rasa_nlu_chi

Turn Chinese natural language into structured data 中文自然语言理解

rekcarc-tsc-uht icon rekcarc-tsc-uht

清华大学计算机系课程攻略 Guidance for courses in Department of Computer Science and Technology, Tsinghua University

snownlp icon snownlp

Python library for processing Chinese text

text_classification icon text_classification

使用rnn,lstm,gru,fasttext,textcnn,dpcnn,rnn-att,lstm-att,bert,bert-cnn,bert-rnn,bert-rcnn,han,xlnet等等做文本分类，以及对比

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