Easy-to-use,Modular and Extendible package of deep learning based CTR(Click Through Rate) prediction models with tensorflow 1.x and 2.x .
Project description
DeepCTR
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
.
Let's Get Started!(Chinese Introduction) and welcome to join us!
Models List
Citation
- Weichen Shen. (2018). 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{shen2018deepctr,
author = {Weichen Shen},
title = {DeepCTR: Easy-to-use,Modular and Extendible package of deep-learning based CTR models},
year = {2018},
publisher = {GitHub},
journal = {GitHub Repository},
howpublished = {\url{https://github.com/shenweichen/deepctr}},
}
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-
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-
wechat ID: deepctrbot
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