DeepCTR is a Easy-to-use,Modular and Extendible package of deep-learning based CTR models along with lots of
core components layers which can be used to easily build custom models.You can use any complex model with model.fit()
,and model.predict()
.
- Provide
tf.keras.Model
like interface for quick experiment . example - Provide
tensorflow estimator
interface for large scale data and distributed training . example - It is compatible with both
tf 1.x
andtf 2.x
.
Some related projects:
- DeepMatch: https://github.com/shenweichen/DeepMatch
- DeepCTR-Torch: https://github.com/shenweichen/DeepCTR-Torch
Let's Get Started!(Chinese Introduction) and welcome to join us!
- Weichen Shen. (2017). DeepCTR: Easy-to-use,Modular and Extendible package of deep-learning based CTR models. https://github.com/shenweichen/deepctr.
If you find this code useful in your research, please cite it using the following BibTeX:
@misc{shen2017deepctr,
author = {Weichen Shen},
title = {DeepCTR: Easy-to-use,Modular and Extendible package of deep-learning based CTR models},
year = {2017},
publisher = {GitHub},
journal = {GitHub Repository},
howpublished = {\url{https://github.com/shenweichen/deepctr}},
}
-
公众号:浅梦学习笔记
-
wechat ID: deepctrbot
Main contributors(welcome to join us!)
![]() Shen Weichen Alibaba Group |
![]() Zan Shuxun Alibaba Group |
![]() Harshit Pande Amazon |
![]() Lai Mincai ShanghaiTech University |
![]() Li Zichao Peking University |
![]() Tan Tingyi Chongqing University |