edaworld

--STM32_MPU9150

academic-drawing

This is a project providing source codes (including Matlab and Python) for presenting experiment results.

activityrecognition

Information about activity recognition

ad5933

ad5933测试程序

awesome_time_series_in_python

This curated list contains python packages for time series analysis

bridge_host

bridge_slave

bridgehost-v2018

2018年，xiaojun改动后去除任务tpc版本

BridgeSlave-mcu

桥梁电阻抗检测下位机项目

Qt-terminalRes

电阻抗测量Qt上位机

edaworld/academic-drawing

This is a project providing source codes (including Matlab and Python) for presenting experiment results.

edaworld/activityrecognition

Information about activity recognition

edaworld/bridgehost-v2018

2018年，xiaojun改动后去除任务tpc版本

edaworld/bridgeslave-v2018

2018年，xiaojun改动后去除任务tpc版本

edaworld/cheatsheets

Official Matplotlib cheat sheets

edaworld/Coursera-ML-AndrewNg-Notes

edaworld/ctw

Implementation of Canonical Time Warping

edaworld/Data-Science--Cheat-Sheet

Cheat Sheets

edaworld/ESKF

Raw data and Matlab implementation of BonaDrone's ESKF

edaworld/ESKF-Attitude-Estimation

Error-State KF algorithm to estimate attitude

edaworld/eskf-gps-imu-fusion

使用误差状态卡尔曼滤波器融合GPS和IMU，实现更高精度的定位

edaworld/feature-selection

edaworld/gtw

Implementation of Generalized Time Warping

edaworld/IMU_Attitude_Estimator

Estimate AHRS attitude with EKF, ESKF and Mahony filter.

edaworld/imu_gnss_fusion

IMU+GNSS Fusion Localization with ESKF

edaworld/IMUCalibration-Gesture

calibration for Imu and show gesture

edaworld/Kalman

Some Python Implementations of the Kalman Filter

edaworld/lstm_anomaly_thesis

Anomaly detection for temporal data using LSTMs

edaworld/Machine-Learning-Classification-Regression-Twitter-Buzz

Buzz Prediction on Twitter: Buzz Prediction on Twitter Project Description: There are two different datasets for Regression and Classification tasks. Right-most column in both the datasets is a dependent variable i.e. buzz. Data description files are also provided for both the datasets. Deciding which dataset is for which task is part of the project. Read data into Jupyter notebook, use pandas to import data into a data frame. Preprocess data: Explore data, check for missing data and apply data scaling. Justify the type of scaling used. Regression Task: Apply all the regression models you've learned so far. If your model has a scaling parameter(s) use Grid Search to find the best scaling parameter. Use plots and graphs to help you get a better glimpse of the results. Then use cross-validation to find average training and testing score. Your submission should have at least the following regression models: KNN regressor, linear regression, Ridge, Lasso, polynomial regression, SVM both simple and with kernels. Finally, find the best regressor for this dataset and train your model on the entire dataset using the best parameters and predict buzz for the test_set. Classification Task: Decide about a good evaluation strategy and justify your choice. Find best parameters for the following classification models: KNN classification, Logistic Regression, Linear Support Vector Machine, Kernelized Support Vector Machine, Decision Tree. Which model gives the best results? Buzz Prediction on Twitter Project Description: Use same datasets as Project 2. Run all the models only on 10% data. Use code given in Project 2 for sampling. Preprocess data: Explore data and apply data scaling. Regression Task: Apply any two models with bagging and any two models with pasting. Apply any two models with adaboost boosting Apply one model with gradient boosting Apply PCA on data and then apply all the models in project 2 again on data you get from PCA. Compare your results with results in project 2. You don't need to apply all the models twice. Just copy the result table from project 2, prepare similar table for all the models after PCA and compare both tables. Does PCA help in getting better results? Apply deep learning models covered in class Classification Task: Apply four voting classifiers - two with hard voting and two with soft voting Apply any two models with bagging and any two models with pasting. Apply any two models with adaboost boosting Apply one model with gradient boosting Apply PCA on data and then apply all the models in project 2 again on data you get from PCA. Compare your results with results in project 2. You don't need to apply all the models twice. Just copy the result table from project 2, prepare similar table for all the models after PCA and compare both tables. Does PCA help in getting better results? Apply deep learning models covered in class

edaworld/Making-elegant-Matlab-figures

A repository comprising multiple functions for making elegant publication-quality figures in MATLAB

edaworld/matlab-plot-tools

Various small packages for Matlab

edaworld/ms2deepscore

Deep learning similarity measure for comparing MS/MS spectra with respect to their chemical similarity

edaworld/techxuexi-js

油猴等插件的 学习强国 js 代码 45分/天

edaworld/traj-dist

A python package for computing distance between 2D trajectories.

edaworld/Transfer-Learning-using-Matlab

Transfer Learning for CNN based Image Classification Networks

edaworld/transferlearning

Everything about Transfer Learning and Domain Adaptation--迁移学习

edaworld/tsfresh

Automatic extraction of relevant features from time series:

edaworld/ttk4250-sensor-fusion

Sensor fusion using bayesian probabilistic methods such as the IMM-PDAF, ESKF and EKF-SLAM. Assignment in the TTK4250 sensor fusion course.

edaworld/twitter-sentiment-analysis

Sentiment analysis on tweets using Naive Bayes, SVM, CNN, LSTM, etc.

edaworld/zotero-better-notes

Everything about note management. All in Zotero.