yao-li57

yao-li57's Stars

• solidglue/Recommender_System

  推荐系统入门指南，全面介绍了工业级推荐系统的理论知识（王树森推荐系统公开课-基于小红书的场景讲解工业界真实的推荐系统），如何基于TensorFlow2训练模型，如何实现高性能、高并发、高可用的Golang推理微服务。Comprehensively introduced the theory of industrial recommender system, how to trainning models based on TensorFlow2, how to implement the high-performance、high-concurrency and high-available inference services base on Golang.

  Language:Jupyter Notebook50444

• datawhalechina/fun-rec

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

  Language:Jupyter Notebook5k864

• Kathy1214/sgt

  搜索、推荐、广告、用增等工业界实践文章收集（来源：知乎、Datafuntalk、技术公众号）

  3

• Hello-MLClub/recommend-Algorithm-Practice

  本项目分享各种类型的推荐算法及实战代码，小白也可轻松掌握

  Language:Python171

• microsoft/BitNet

  Official inference framework for 1-bit LLMs

  Language:C++12.6k881

• bigbully/Dapper-translation

  translate the paper of "Dapper, a Large-Scale Distributed Systems Tracing Infrastructure"

  1.2k270

• floodsung/Deep-Learning-Papers-Reading-Roadmap

  Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!

  Language:Python38.6k7.3k

• Vu5e/JobFailurePredictionGoogleTraces2019

  By learning and using prediction for failures, it is one of the important steps to improve the reliability of the cloud computing system. Furthermore, gave the ability to avoid incidents of failure and costs overhead of the system. It created a wonderful opportunity with the breakthroughs of machine learning and cloud storage that utilize generated huge data that provide pathways to predict when the system or hardware malfunction or fails. It can be used to improve the reliability of the system with the help of insights of using statistical analysis on the workload data from the cloud providers. This research will discuss regarding job usage data of tasks on the large “Google Cluster Workload Traces 2019” dataset, using multiple resampling techniques such as “Random Under Sampling, Random Oversampling and Synthetic Minority Oversampling Technique” to handle the imbalanced dataset. Furthermore, using multiple machine learning algorithm which is for traditional machine learning algorithm are “Logistic Regression, Decision Tree Classifier, Random Forest Classifier, Gradient Boosting Classifier and Extreme Gradient Boosting Classifier” while deep learning algorithm using “Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU)” for job failure prediction between imbalanced and balanced dataset. Then, to have a comparison of imbalanced and balanced in terms of model accuracy, error rate, sensitivity, f – measure, and precision. The results are Extreme Gradient Boosting Classifier and Gradient Boosting Classifier is the most performing algorithm with and without imbalanced handling techniques. It showcases that SMOTE is the best method to choose from for handling imbalanced data. The deep learning model of LSTM and Gated Recurrent Unit may be not the best for the in terms of accuracy, based on the ROC Curve its better than the XGBoost Classifier and Gradient Boosting Classifier.

  Language:Jupyter Notebook12

• pengsida/learning_research

  本人的科研经验

  6.3k374

• xiaoweiChen/Cpp_Concurrency_In_Action

  :book: 作为对《C++ Concurrency in Action》英文版的中文翻译。

  2k611

• itisyang/ImageMiniLab

  opencv-python 应用

  Language:Python11329

• geekcompany/ResumeSample

  Resume template for Chinese programmers . 程序员简历模板系列。包括PHP程序员简历模板、iOS程序员简历模板、Android程序员简历模板、Web前端程序员简历模板、Java程序员简历模板、C/C++程序员简历模板、NodeJS程序员简历模板、架构师简历模板以及通用程序员简历模板

  27.4k9k

• HDU-Course/.github

  GitHub Profile for HDU-Course

  1

• JaceyRx/Examination_System

  一个简单的教务查询系统（主要技术SpringMVC + Spring + Mybatis + Shiro + Bootstrap）

  Language:Java1.4k527

• HDU-Course/HDU-FinalExamPaper

  杭州电子科技大学 HDU 期末考试卷 杭电 资料分享

  14224

• Fafa-DL/Lhy_Machine_Learning

  李宏毅2021/2022/2023春季机器学习课程课件及作业

  Language:Jupyter Notebook6.4k1.6k

• Miraclelucy/dive_into_deep_learning

  ✔️李沐 【动手学深度学习】课程学习笔记：使用pycharm编程，基于pytorch框架实现。

  Language:Python2.6k471

• Jack-Cherish/Deep-Learning

  :computer:深度学习实战：手写数字识别、Discuz验证码识别、垃圾分类、语义分割

  Language:Python2.2k998

• robbertliu/deeplearning.ai-andrewNG

  deeplearning.ai , By Andrew Ng, All slide and notebook + data + solutions and video link

  Language:Jupyter Notebook1.5k551

• ShusenTang/Dive-into-DL-PyTorch

  本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为PyTorch实现。

  Language:Jupyter Notebook18.6k5.4k

• CaptainLYN/Andrew-NG-Meachine-Learning

  coursera中Andrew Ng的meachine learning的所有编程测验的原文件

  569205

• FengGuanxi/HDU-Experience

  用于向所有杭电学子分享在杭电的知识与经验

  Language:Jupyter Notebook772180

• zitalk/NYBike

  大三项目实训

  Language:Java71