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7days-golang

7 days golang programs from scratch (web framework Gee, distributed cache GeeCache, object relational mapping ORM framework GeeORM, rpc framework GeeRPC etc) 7天用Go动手写/从零实现系列

944.life

996.ICU 的反向 repo: 944 工作制 - 工作 944，生活为先

955.wlb

955 不加班的公司名单 - 工作 955，work–life balance (工作与生活的平衡)

addernet

Code for paper " AdderNet: Do We Really Need Multiplications in Deep Learning?"

advanced-go-programming-book

:books: 《Go语言高级编程》开源图书，涵盖CGO、Go汇编语言、RPC实现、Protobuf插件实现、Web框架实现、分布式系统等高阶主题(完稿)

advanced-java

😮 Core Interview Questions & Answers For Experienced Java(Backend) Developers | 互联网 Java 工程师进阶知识完全扫盲：涵盖高并发、分布式、高可用、微服务、海量数据处理等领域知识

awesome-fenix

讨论如何构建一套可靠的大型分布式系统

awesome-java

Collection of awesome Java project on Github(非常棒的 Java 开源项目集合).

bigdata-notes

大数据入门指南 :star:

codeforces-go

Golang 算法竞赛模板库 | Solutions to Codeforces by Go 💭💡🎈

data-analysis

Data Science Using Python

data-analysis-and-machine-learning-projects

Repository of teaching materials, code, and data for my data analysis and machine learning projects.

datasciencepython

common data analysis and machine learning tasks using python

deap-granger-causality-emotion-classification

The basic idea of old JRP idea is the calculation of the synchronization index which describes the generalized synchronization between two systems. Between VAR and granger Casualty, the VAR can be considered as a means of conducting causality tests, or more specifically Granger causality tests. The VAR model shows that one EEG channel has an influence on the other given channel; however Granger causality really implies a correlation between the current value of one variable and the past values of others, it does not mean changes in one variable cause changes in another.

deap-jrp-emotion-classification

Emotional Classification with the DEAP dataset using EEGLAB, matlab and python. Currently in the status of developing a more efficient and high accuracy method for emotion classification using EEG data regardless of number of channels.

eeg

PyTorch EEG emotion analysis using DEAP dataset

eeg-based-emotion-analysis-using-deap-dataset-for-supervised-machine-learning

This project is for classification of emotions using EEG signals recorded in the DEAP dataset to achieve high accuracy score using machine learning algorithms such as Support vector machine and K - Nearest Neighbor.

eeg-emotion-classification

eeg-emotion-recognition

EEG data processing and it's convolution using AutoEncoder + CNN + RNN

eeg-emotion-recognition-domain-subject-adaptation

EEG Emotion Recognition project, experiment on SEED (SEED-IV), DEAP dataset

eeg_classify

Some attempts to classify EEG signals from the BCI competition

eeg_emotion_classification_use_cnn-and-rnn

eeg_emotion_classifier_deap

EEG Emotion classification using the DEAP pre-processed data

eladmin

项目基于 Spring Boot 2.1.0 、 Jpa、 Spring Security、redis、Vue的前后端分离的后台管理系统，项目采用分模块开发方式， 权限控制采用 RBAC，支持数据字典与数据权限管理，支持一键生成前后端代码，支持动态路由

emotion-classification

Feature selection via dependence maximization for EEG based emotion classification.

emotion-recognition

Emotion recognition using DEAP dataset，GSR。

emotion-recognition-1

Emotion recognition models based on DEAP data

emotion-recognition-by-deap-dataset

Repo for final year project

emotion-recognition-deap_gsr

emotion-recogniton-from-eeg-signals

Emotion Recognition from EEG Signals using the DEAP dataset with 86.4% accuracy. Applied multiple machine learning models and implemented various signal transforming algorithms like the DWT algorithm.