sangyongjia's repositories
stock
stock股票.获取股票数据,计算股票指标,识别股票形态,综合选股,选股策略,股票验证回测,股票自动交易,支持PC及移动设备。
DeepCTR
Easy-to-use,Modular and Extendible package of deep-learning based CTR models .
vector-provision-options
Tutorial to demonstrate different ways of providing vectors for Weaviate objects
Factor-Investing-CN
感谢石川等大佬的著作《因子投资-方案与实际》,本repo将尝试作为补充,为各个概念提供说明,以及尝试提供实现部分代码
EasyRec
A framework for large scale recommendation algorithms.
GraphEmbedding
Implementation and experiments of graph embedding algorithms.
eat_pyspark_in_10_days
pyspark🍒🥭 is delicious,just eat it!😋😋
eat_tensorflow2_in_30_days
Tensorflow2.0 🍎🍊 is delicious, just eat it! 😋😋
py-mlfactor
Rewriting the code in "Machine Learning for Factor Investing" in Python
LeetCodeCplusplus
For improving myself
AutoFIS
AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction
x-deeplearning
An industrial deep learning framework for high-dimension sparse data
DeepMatch
A deep matching model library for recommendations & advertising. It's easy to train models and to export representation vectors for user and item which can be used for ANN search.
datasketch
MinHash, LSH, LSH Forest, Weighted MinHash, HyperLogLog, HyperLogLog++, LSH Ensemble
ML-NLP
此项目是机器学习(Machine Learning)、深度学习(Deep Learning)、NLP面试中常考到的知识点和代码实现,也是作为一个算法工程师必会的理论基础知识。
KDTree_BallTree
Python implement of KD_Tree and Ball_Tree
2019-sinuokeRules
欢迎issue和PR
iQIYI-VID-2019-TOP4
TOP4 solution of 2019 iQIYI Celebrity Video Identification Challenge
DeepNeuralNetworksforYouTubeRecommendationsImpl
youtube video recommendation(generation 4)
tensorflow2_tutorials_chinese
tensorflow2中文教程,持续更新(当前版本:tensorflow2.0),tag: tensorflow 2.0 tutorials
recommenders
Best Practices on Recommendation Systems
tensorflow_practice
tensorflow实战练习,包括强化学习、推荐系统、nlp等
kdtree
A Python implementation of a kd-tree
Reco-papers
Classic papers and resources on recommendation
Paper-Implementation-Matrix-Factorization-Recommender-Systems-Netflix
[Python3.6] IEEE Paper "Matrix Factorization Techniques for Recommender Systems" by Koren,Bell,Volinsky