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
tf.keras.Modellike interface for quick experiment. example
tensorflow estimatorinterface for large scale data and distributed training. example
- It is compatible with both
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
10/15/2022 : Support python 3.9,`3.10`. Changelog
06/11/2022 : Improve compatibility with tensorflow 2.x. Changelog
- Classification: Criteo
- Classification: Criteo with feature hashing on the fly
- Regression: Movielens
- Multi-value Input : Movielens
- Multi-value Input : Movielens with feature hashing on the fly
- Hash Layer with pre-defined key-value vocabulary
- Estimator with TFRecord: Classification Criteo
- Estimator with Pandas DataFrame: Classification Criteo
- MultiTask Learning:MMOE
- 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