Welcome to DeepCTR’s documentation!¶
DeepCTR is a Easy-to-use , Modular and Extendible package of deep-learning based CTR models along with lots of core components layer which can be used to easily build custom models.You can use any complex model with model.fit() and model.predict().
- Provide
tf.keras.Modellike interface for quick experiment. example - Provide
tensorflow estimatorinterface for large scale data and distributed training. example - It is compatible with both
tf 1.xandtf 2.x.
Let’s Get Started! (Chinese Introduction)
You can read the latest code and related projects
- DeepCTR: https://github.com/shenweichen/DeepCTR
- DeepMatch: https://github.com/shenweichen/DeepMatch
- DeepCTR-Torch: https://github.com/shenweichen/DeepCTR-Torch
News¶
10/11/2020 : Refactor DNN Layer. Changelog
09/12/2020 : Improve the reproducibility & fix some bugs. Changelog
06/27/2020 : Support Tensorflow Estimator for large scale data and distributed training.Support different initializers for different embedding weights and loading pretrained embeddings.Add new model FwFM. Changelog
Cooperative promotion 合作推广¶
For more information about the recommendation system, such as feature engineering, user profile, matching, ranking and multi-objective optimization, online learning and real-time computing, and more cutting-edge technologies and practical projects :
更多关于推荐系统的内容，如 特征工程，用户画像，召回，排序和多目标优化，在线学习与实时计算以及更多前沿技术和实战项目 等可参考：
Home:
- Quick-Start
- Features
- Examples
- FAQ
- 1. Save or load weights/models
- 2. Set learning rate and use earlystopping
- 3. Get the attentional weights of feature interactions in AFM
- 4. How to extract the embedding vectors in deepfm?
- 5. How to add a long dense feature vector as a input to the model?
- 6. How to use pretrained weights to initialize embedding weights and frozen embedding weights?
- 7. How to run the demo with GPU ?
- 8. How to run the demo with multiple GPUs
- History
API: