Implementation of JNSKR: Jointly Non-Sampling Learning for Knowledge Graph Enhanced Recommendation (SIGIR 2020) in PyTorch
The source code and dataset about <Deep Learning - Best Practices on TensorFlow Engineering Implementation>
pytorch handbook是一本开源的书籍，目标是帮助那些希望和使用PyTorch进行深度学习开发和研究的朋友快速入门，其中包含的Pytorch教程全部通过测试保证可以成功运行
1. Use BERT, ALBERT and GPT2 as tensorflow2.0's layer. 2. Implement GCN, GAN, GIN and GraphSAGE based on message passing.
TensorFlow 2.x version's Tutorials and Examples, including CNN, RNN, GAN, Auto-Encoders, FasterRCNN, GPT, BERT examples, etc. TF 2.0版入门实例代码，实战教程。
深度学习入门开源书，基于TensorFlow 2.0案例实战。Open source Deep Learning book, based on TensorFlow 2.0 framework.
WebGL-accelerated ML // linear algebra // automatic differentiation for JavaScript.
PyTorch implementation of "Efficient Neural Architecture Search via Parameters Sharing"
Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
