liaozp1

llm-cookbook

面向开发者的 LLM 入门教程，吴恩达大模型系列课程中文版

so-large-lm

大模型基础: 一文了解大模型基础知识

Home_Credit_Default_Risk_my_notebook

在Kaggle比赛中的Featuretools指南

apachecn-dl-zh

ApacheCN 深度学习译文集

Code

运小筹公众号是致力于分享运筹优化(LP、MIP、NLP、随机规划、鲁棒优化)、凸优化、强化学习等研究领域的内容以及涉及到的算法的代码实现。

Training

运筹优化入门培训资料, 以启发式算法为主.

or-tools

Google's Operations Research tools:

MorvanZhou-Reinforcement-learning-with-tensorflow

Reinforcement-learning-with-tensorflow

Simple Reinforcement learning tutorials, 莫烦Python 中文AI教学

lifelines

Survival analysis in Python

hello-algorithm

🌍 针对小白的算法训练 | 包括四部分：①.大厂面经 ②.力扣图解 ③.千本开源电子书 ④.百张技术思维导图（项目花了上百小时，希望可以点 star 支持，🌹感谢~）推荐免费ChatGPT使用网站

fucking-algorithm

刷算法全靠套路，认准 labuladong 就够了！English version supported! Crack LeetCode, not only how, but also why.

Learn-Algorithms

算法学习笔记

daily-interview

Datawhale成员整理的面经，内容包括机器学习，CV，NLP，推荐，开发等，欢迎大家star

fun-rec

推荐系统入门教程，在线阅读地址：https://datawhalechina.github.io/fun-rec/

DeepCTR

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

AlgoNotes

【浅梦学习笔记】文章汇总:包含 排序&CXR预估，召回匹配，用户画像&特征工程，推荐搜索综合 计算广告，大数据，图算法，NLP&CV，求职面试 等内容

SparkCTR

CTR prediction model based on spark(LR, GBDT, DNN)

recommendation_model_master

自己学习推荐系统过程中用到的代码

Ad-papers

Papers on Computational Advertising

SparrowRecSys

A Deep Learning Recommender System

RSPapers

RSTutorials: A Curated List of Must-read Papers on Recommender System.

LeetcodeTop

汇总各大互联网公司容易考察的高频leetcode题🔥

learning_to_rank

利用lightgbm做(learning to rank)排序学习，包括数据处理、模型训练、模型决策可视化、模型可解释性以及预测等。Use LightGBM to learn ranking, including data processing, model training, model decision visualization, model interpretability and prediction, etc.

xgboost

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow

xgb-scala

learn_LTR

LTR学习代码

TouTiaoRecoSysCourseware

黑马头条推荐系统

models

Models and examples built with TensorFlow

system-design-primer

Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.