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  • 👋 Hi, I’m @Acveah
  • 👀 I’m interested in Machine Learning & Microservice
  • 🌱 I’m currently learning Machine Learning / Algorithm
  • 💞️ I’m looking to collaborate with someone like you
  • 📫 How to reach me [email protected]

Yjl_cc's Projects

acveah icon acveah

Config files for my GitHub profile.

adaptivemerging icon adaptivemerging

This project includes the simulation for adaptive merging of video processing services.

blottogame icon blottogame

A program to find the best arrangement of resources in the Colonel Blotto Game by genetic algorithm

botnet-detection icon botnet-detection

Topological botnet detection datasets and graph neural network applications

campus_recruitmen_questions icon campus_recruitmen_questions

2021年最新整理,5000道秋招/提前批/春招/常用面试题(含答案),包括leetcode,校招笔试题,面试题,算法题,语法题。持续更新中

cloudsim-projects icon cloudsim-projects

A project using CloudSim to simulate cloud scheduling and resource provisioning for video transcoding

cloudsimpy icon cloudsimpy

CloudSimPy: Datacenter job scheduling simulation framework

cosco icon cosco

[TPDS'21] COSCO: Container Orchestration using Co-Simulation and Gradient Based Optimization for Fog Computing Environments

cs-notes icon cs-notes

:books: 技术面试必备基础知识、Leetcode、计算机操作系统、计算机网络、系统设计、Java、Python、C++

dagmm icon dagmm

My attempt at reproducing the paper Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection

ddos-attacks-detection-using-svm-model icon ddos-attacks-detection-using-svm-model

In this Project, A simulation of a network traffic is created to track and capture the patterns from any type of DDoS attacks with the help of SVM model. By using rule detection and SVM model together, the results deliver the details of the attack. This helps in improving the detection rate of the DDoS attacks from flooding or crashing the user’ services.

ddos-sdn icon ddos-sdn

early detection and mitigation of DDos using centralized SDN controller POX

deepadots icon deepadots

Repository of the paper "A Systematic Evaluation of Deep Anomaly Detection Methods for Time Series".

deeplearning-500-questions icon deeplearning-500-questions

深度学习500问,以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述,以帮助自己及有需要的读者。 全书分为15个章节,近20万字。由于水平有限,书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作,联系[email protected] 版权所有,违权必究 Tan 2018.06

dev-sidecar icon dev-sidecar

开发者边车,github打不开,github加速,git clone加速,git release下载加速,stackoverflow加速

dive-into-dl-pytorch icon 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。

dive-into-dl-tensorflow2.0 icon dive-into-dl-tensorflow2.0

本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为TensorFlow 2.0实现,项目已得到李沐老师的认可

dlsf icon dlsf

[TMC'20] Deep Learning based Scheduler for Stochastic Fog-Cloud computing environments

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