Easy-to-use,Modular and Extendible package of deep-learning based CTR models .

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Readme

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 interfaces for**quick experiment**. example - Provide
`tensorflow estimator`

interface for**large scale data**and**distributed training**. example - It is compatible with both
`tf 1.x`

and`tf 2.x`

.

Some related projects:

- DeepMatch: shenweichen/DeepMatch
- DeepCTR-Torch: 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. 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}},
}
```

- Github Discussions
- Wechat Discussions

公众号：浅梦学习笔记 | 微信：deepctrbot | 学习小组 加入 主题集合 |
---|---|---|

Shen Weichen Alibaba Group |
Zan Shuxun Alibaba Group |
Harshit Pande Amazon |
Lai Mincai ByteDance |
Li Zichao ByteDance |
Tan Tingyi Chongqing University |

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