Source code and dataset for KDD 2020 paper "Controllable Multi-Interest Framework for Recommendation"

An index of recommendation algorithms that are based on Graph Neural Networks. (TORS)

embedx 是基于 c 开发的、完全自研的分布式 embedding 训练和推理框架。它目前支持 图模型、深度排序、召回模型和图与排序、图与召回的联合训练模型等

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A framework for large scale recommendation algorithms.

A scalable nearest neighbor search library in Apache Spark

This is an implementation of sgemm_kernel on L1d cache.

A distributed graph deep learning framework.

Curating a list of AutoML-related research, tools, projects and other resources

My implementation of the original GAT paper (Veličković et al.). I've additionally included the playground.py file for visualizing the Cora dataset, GAT embeddings, an attention mechanism, and entr…

Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach, AAAI2020

deepx_core是一个专注于张量计算/深度学习的基础库

PyTorch implementation of MoCo: https://arxiv.org/abs/1911.05722

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

强化学习中文教程（蘑菇书🍄），在线阅读地址：https://datawhalechina.github.io/easy-rl/

互联网首份程序员考公指南，由3位已经进入体制内的前大厂程序员联合献上。

Faster and elegant TensorFlow Implementation of paper: Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems

analyze the source code of word2vec

📚 免费的计算机编程类中文书籍，欢迎投稿

Pytorch implementation of the GradNorm. GradNorm addresses the problem of balancing multiple losses for multi-task learning by learning adjustable weight coefficients.

NCCL Tests

Mesh TensorFlow: Model Parallelism Made Easier

Stores documents used by the TensorFlow developer community

Implementation of Graph Convolutional Networks in TensorFlow

Graph Convolutional Networks in PyTorch

OneFlow is a deep learning framework designed to be user-friendly, scalable and efficient.

C associative containers

Abseil Common Libraries (C )

Papers for normalization techniques, released codes collections.