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Name: 周奇
Type: User
Name: 周奇
Type: User
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100 Days of ML Coding
100-Days-Of-ML-Code中文版
12306智能刷票,订票
Applied Deep Learning @National Taiwan University
A Discourse-Level Named Entity Recognition and Relation Extraction Dataset for Chinese Literature
AGDISTIS - Agnostic Named Entity Disambiguation
对知识库Wikidata的爬虫以及数据处理脚本 将三元组关系对齐到语料库的脚本 获取知识图谱数据的脚本
海量中文预训练ALBERT模型, A LITE BERT FOR SELF-SUPERVISED LEARNING OF LANGUAGE REPRESENTATIONS
2018/2019/校招/春招/秋招/算法/机器学习(Machine Learning)/深度学习(Deep Learning)/自然语言处理(NLP)/C/C++/Python/面试笔记
Bug-tracking for Jeff's algorithms book, notes, etc.
Notebooks for the Algorthmic Machine Learning class @ Eurecom
Goal: To understand the Wikipedia dataset, especially the entity info boxes. Task: We have taken the Wikipedia dump. Our aim is to extract information about various entity types. The steps for this task are as follows: 1. Given the Wikipedia dump, gather all the pages from Wikipedia with Info boxes on them. 2. Find the set of all possible entity types on Wikipedia 3. Find the set of all possible attributes that can be associated with any entity type on Wikipedia. 4. From a few values of these attributes, infer the data type of these attributes as one of the following: String, set of strings, duration, number, set of durations, date, other. 5. Find various units that can be used to express the value of a numeric attribute. E.g., for “height” attribute of “person” entities, the units could be “cms, inches” 6. For numeric attributes, find typical ranges (using the most popular unit). E.g., For person entities, the age attribute should have the range as 0-150 years. 7. For attributes which are semantically similar but have different names used across different entities of the same type, merge them. E.g., Automatically identify that the attribute “birthdate” is the same as “bdate”.
Codes related to activities on AV including articles, hackathons and discussions.
Public and free annotated datasets of relationships between entities/nominals
接口自动化测试平台
Use the Apollo EM algorithm (a variation with multi-value variables) to solve the slot filling problem in NLP.
Arbitrary image stylization using TensorFlow.js
some attention implements
Experimenting with Attention models and Memory Networks
注意力机制on自然语言处理文章整理笔记
keras结合Attention机制用CNN和LSTM进行句子分类
All about attention in neural networks. Soft attention, attention maps, local and global attention and multi-head attention.
Cross-platform Beanstalk queue server console.
Web Of Science Author Name Disambiguation
Text autoencoder with LSTMs
算法工程师(人工智能CV方向)面试问题及相关资料
精选 OpenAI 的 [ChatGPT](https://chat.openai.com) 资源清单, 跟随最新资源并添加中文相关Work
A curated list of resources for Chinese NLP 中文自然语言处理相关资料
:memo: An awesome Data Science repository to learn and apply for real world problems.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.