Mahmood Yadegari's repositories
SmartGridFraudDetection
Electricity Fraud Detection in Smart Grids
eif
Extended Isolation Forest for Anomaly Detection
my_ml_service-1
My Machine Learning Web Service
python_for_image_processing_APEER
https://www.youtube.com/playlist?list=PLHae9ggVvqPgyRQQOtENr6hK0m1UquGaG
Datacamp_Machine_Learning_Projects
Machine leaning projects
prophet
Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
TecoGAN
This repo will contain source code and materials for the TecoGAN project, i.e. code for a TEmporally COherent GAN
dimensionality-reduction-autoencoders
2D convolutional autoencoder and variational autoencoder implementation for tutorials.
Coursera-Machine-Learning-Stanford
Machine learning-Stanford University
Time-Series-Forcasting
In this repository i have implemented various Deep Learning multivariate and multiheaded time series forecasting models . Apart from that i have also uploaded the Ipython file of Grid_Search and Ensemble_Learning technique which i have implemented during my summer intern of IIT-Mandi(May 2019)
VideoSuperResolution
A collection of state-of-the-art video or single-image super-resolution architectures, reimplemented in tensorflow.
generative-compression
TensorFlow Implementation of Generative Adversarial Networks for Extreme Learned Image Compression
pyqt5-qtquick2-example
An example of QtQuick 2 providing material and fluent design themes in PyQt5.
car-damage-assessment
Computer Vision and Deep Learning techniques to accurately classify vehicle damage to facilitate claims triage by training convolution neural networks
Semantic-Segmentation-Suite
Semantic Segmentation Suite in TensorFlow. Implement, train, and test new Semantic Segmentation models easily!
Variational-Lstm-Autoencoder
Lstm variational auto-encoder API for time series anomaly detection and features extraction
Electricity_Fraud_Detection
The main objective of this project is to solve the manual billing of electricity and tackle the electricity leakage by finding the charlatan via their regular electricity consumption using data analysis and machine learning algorithms.
eye-writing-easy
Simple project of eye-writing, using machine learning-based facial mapping (landmarks).
Advanced_Data-Science_with_IBM_Specialization
Advanced Data Science with IBM Specialization
Persian-Sentiment-Resources
Awesome Persian Sentiment Analysis Resources - منابع مرتبط با تحلیل احساسات در زبان فارسی
Time-Series-Analysis-and-Forecasting
End To End Tutorial on Time Series Analysis and Forcasting
table-parser-opencv
Extract tables from images or PDFs and convert them to Excel files
Content-adaptive-superpixel-segmentation
Code of content-adaptive superpixel segmentation, published in TIP, 2018.
TII_Wide-Deep_Electricity_Theft_Detection
This is the source code of our paper on electricity-theft detection published in TII in the 2017 year.
electricity-theft-detection-with-self-attention
Electricity theft detection using Self-Attention mechanisms
Hybrid-Beamforming-for-Millimeter-Wave-Systems-Using-the-MMSE-Criterion
The Matlab Simulation codes for Hybrid Beamforming for Millimeter Wave Systems Using the MMSE Criterion.
Instagram_Comments_Web_Scraping
Instagram and Youtube comments scraping using selenium and BeautifulSoup
DAGAN
The implementation code for "DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction"
Anomaly-detection-based-on-multiple-streaming-sensor-data
Today, the Internet of Things is widely used in various fields, such as factories, public facilities, and even homes. The use of the Internet of Things involves a large number of sensor devices that collect various types of data in real time, such as machine voltage, current, and temperature. These devices will generate a large amount of streaming sensor data. These data can be used to make the data analysis, which can discover hidden relation such as monitoring operating status of a machine, detecting anomalies and alerting the company in time to avoid significant losses. Therefore, the application of anomaly detection in the field of data mining is very extensive.