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¶
11/10/2022 : Add EDCN . Changelog
10/15/2022 : Support python 3.9 , 3.10 . Changelog
06/11/2022 : Improve compatibility with tensorflow 2.x. Changelog
DisscussionGroup¶
公众号：浅梦学习笔记 wechat ID: deepctrbot
Home:
- Quick-Start
- Features
- Examples
- 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
- 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