linzhk
主要项目和研究方向: 1、校园闲置物品平台搭建,商业化及运营(2015-2018,http://sysu-xianzhi.com/,截止至2018年用户数超2w,平台第三年盈利超17w) 2、第三方支付业务(2018腾讯产品实习,微信支付香港钱包落地) 3、自然语言处理(硕士研究方向)
中国深圳
Pinned Repositories
-Chinese-Science-Fiction-Dataset-
Chinese Science Fiction Dataset
awesome-text-generation
A curated list of recent models of text generation and application
blockchain
区块链 - 中文资源
c-and-cpp-language-learning
C和C++编程语言学习 - 2015级
chinese-latex-resume
全中文汉化latex简历。支持overleaf个性化编辑并生成pdf。适用于互联网求职产品、运营、算法、开发岗
DeepRec
DeepRec is a recommendation engine based on TensorFlow.
dgl
Python package built to ease deep learning on graph, on top of existing DL frameworks.
fiction-sharing
workplace
Graph-Nueral-Network-and-Few-Shot-Learning-in-NLU-A-Survey
图网络和少样本学习在自然语言理解领域的前沿综述。本文旨在探索自然语言理解领域中(主要探索了命名实体识别,关系抽取)一些深度学习前沿的应用(主要探索了结合图神经网络和少样本学习场景的方法)共计15篇顶会论文(EMNLP,AAAI,ACL,COLING)。
Tianchi-Text-Classification-Counter-Attack-Project
linzhk's Repositories
linzhk/chinese-latex-resume
全中文汉化latex简历。支持overleaf个性化编辑并生成pdf。适用于互联网求职产品、运营、算法、开发岗
linzhk/Tianchi-Text-Classification-Counter-Attack-Project
linzhk/Graph-Nueral-Network-and-Few-Shot-Learning-in-NLU-A-Survey
图网络和少样本学习在自然语言理解领域的前沿综述。本文旨在探索自然语言理解领域中(主要探索了命名实体识别,关系抽取)一些深度学习前沿的应用(主要探索了结合图神经网络和少样本学习场景的方法)共计15篇顶会论文(EMNLP,AAAI,ACL,COLING)。
linzhk/-Chinese-Science-Fiction-Dataset-
Chinese Science Fiction Dataset
linzhk/awesome-text-generation
A curated list of recent models of text generation and application
linzhk/blockchain
区块链 - 中文资源
linzhk/c-and-cpp-language-learning
C和C++编程语言学习 - 2015级
linzhk/DeepRec
DeepRec is a recommendation engine based on TensorFlow.
linzhk/dgl
Python package built to ease deep learning on graph, on top of existing DL frameworks.
linzhk/fiction-sharing
workplace
linzhk/Gitbook
linzhk/linzhk.github.io
linzhk/Planning-based-Hierarchical-Variational-Model
Dataset and code for EMNLP 2019
linzhk/plms-graph2text
Investigating Pretrained Language Models for Graph-to-Text Generation
linzhk/qcloud-documents
腾讯云官方文档 使用Markdown自动构建
linzhk/Recommendation
1. 记录推荐系统前沿的算法和工程实践
linzhk/transformers
🤗 Transformers: State-of-the-art Natural Language Processing for TensorFlow 2.0 and PyTorch.