I have 49 projects on Github
文本相似度计算/文本匹配
文本分类:传统机器学习模型和深度学习模型
实现了谱聚类的算法。
数据集
This repo contains the source code in my personal column (https://zhuanlan.zhihu.com/zhaoyeyu), implemented using Python 3.6. Including Natural Language Processing and Computer Vision projects, such as text generation, machine translation, deep convolution GAN and other actual combat code.
推荐
算法比赛-转化率预估、购买预测
文本分类
Spark 编程指南简体中文版
该项目是短文本分类,目前应用于新闻标签的分类
RISC-V Functional ISA Simulator
An elegant \LaTeX\ résumé template
PArallel Distributed Deep LEarning (『飞桨』核心框架,高性能单机、分布式训练和跨平台部署)
OnlineAggregationOnSpark
TensorFlow Neural Machine Translation Tutorial
Model Conversion, Compression and Acceleration
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的深度学习模型)
A 5-level pipelined CPU described with Verilog, supports 50 MIPS instructions and interruption
Intel SGX for Linux*
Describe past Kaggle solutions
2017年买房经历总结出来的买房购房知识分享给大家,希望对大家有所帮助。买房不易,且买且珍惜
A lightweight graph computation platform in C/C++
NLP made easy
实现了遗传算法。
Documentations for PaddlePaddle
Feature selector is a tool for dimensionality reduction of machine learning datasets
Features selection algorithm based on self selected algorithm, loss function and validation method
PyTorch/Keras/TensorFlow 编程
深度学习500问,以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述,以帮助自己及有需要的读者。 全书分为18个章节,近30万字。由于水平有限,书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作,联系scutjy2015@163.com 版权所有,违权必究 Tan 2018.06
Easy-to-use,Modular and Extendible package of deep-learning based CTR models.DeepFM,DeepInterestNetwork(DIN),DeepCrossNetwork(DCN),AttentionalFactorizationMachine(AFM),Neural Factorization Machine(NFM)
Convolutional Neural Networks
Crime assistant including crime type prediction and crime consult service based on nlp methods and crime kg,罪名法务智能项目,内容包括856项罪名知识图谱, 基于280万罪名训练库的罪名预测,基于20W法务问答对的13类问题分类与法律资讯问答功能.
计算机视觉
中文命名实体识别
Deep Learning 101 with PaddlePaddle (『飞桨』深度学习框架入门教程)
Stony Brook University CFI library
A curated list of SGX code and resources.
FAQ-based Question Answering System
算法题总结
Papers on Computational Advertising