luyao777

the-algorithm

Source code for Twitter's Recommendation Algorithm

llama.cpp

LLM inference in C/C++

nanoGPT

The simplest, fastest repository for training/finetuning medium-sized GPTs.

carbon-lang

Carbon Language's main repository: documents, design, implementation, and related tools. (NOTE: Carbon Language is experimental; see README)

C-Plus-Plus

Collection of various algorithms in mathematics, machine learning, computer science and physics implemented in C++ for educational purposes.

spdlog

Fast C++ logging library.

recommenders

Best Practices on Recommendation Systems

the-algorithm-ml

Source code for Twitter's Recommendation Algorithm

CppTemplateTutorial

中文的C++ Template的教学指南。与知名书籍C++ Templates不同，该系列教程将C++ Templates作为一门图灵完备的语言来讲授，以求帮助读者对Meta-Programming融会贯通。(正在施工中)

CS-Xmind-Note

计算机专业课（408）思维导图和笔记：计算机组成原理（第五版 王爱英），数据结构（王道），计算机网络（第七版 谢希仁），操作系统（第四版 汤小丹）

QUANTAXIS

QUANTAXIS 支持任务调度 分布式部署的 股票/期货/期权 数据/回测/模拟/交易/可视化/多账户 纯本地量化解决方案

RecBole

A unified, comprehensive and efficient recommendation library

spotlight

Deep recommender models using PyTorch.

cnblogs-theme-silence

📖 一款专注于阅读的博客园主题

addons

Useful extra functionality for TensorFlow 2.x maintained by SIG-addons

EasyRec

A framework for large scale recommendation algorithms.

CV_interviews_Q-A

CV算法岗知识点及面试问答汇总，主要分为计算机视觉、机器学习、图像处理和 C++基础四大块，一起努力向offers发起冲击！

DeepRec

DeepRec is a high-performance recommendation deep learning framework based on TensorFlow. It is hosted in incubation in LF AI & Data Foundation.

Awesome-RSPapers

Recommender System Papers

FuxiCTR

A configurable, tunable, and reproducible library for CTR prediction https://fuxictr.github.io/tutorials

RecommendSystemPractice

《推荐系统实践》代码实现

qtrader

A Light Event-Driven Algorithmic Trading Engine

aioquant

Asynchronous event I/O driven quantitative trading framework.

awesome-quant

**的Quant相关资源索引

lgssl

[CVPR 2023] Learning Visual Representations via Language-Guided Sampling

HierarchicalKV

HierarchicalKV is a part of NVIDIA Merlin and provides hierarchical key-value storage to meet RecSys requirements. The key capability of HierarchicalKV is to store key-value feature-embeddings on high-bandwidth memory (HBM) of GPUs and in host memory. It also can be used as a generic key-value storage.

trade_strategy

用backtrader实现一些交易策略的回测。

SML

This is an implementation for our SIGIR 2020 paper: How to Retrain Recommender System? A Sequential Meta-Learning Method.

MFGAN

deeplite

deeplearning models implemented by pytorch FM, FFM, DeepFM, Wide&Deep, DCN, XDeepFM, FastText, TextCNN