Pinned Repositories
Age-and-Gender-Estimation
Age and Gender Estimation
Detecting-Parkinson-s-Disease--Machine-Learning.
This dataset is composed of a range of biomedical voice measurements from 31 people, 23 with Parkinson's disease (PD). Each column in the table is a particular voice measure, and each row corresponds to one of 195 voice recordings from these individuals ("name" column). The main aim of the data is to discriminate healthy people from those with PD, according to the "status" column which is set to 0 for healthy and 1 for PD.
Flask-SSLChatApp
Task_scheduling
Implementaion of "Energy and Performance-Aware Task Scheduling in a Mobile Cloud Computing Environment"
Bright-Spot-Detection
Bright Spot Detection using OpenCV
code_snippets
data
Data and code behind the articles and graphics at FiveThirtyEight
Event-based-data
Event-based data for compression
Face-and-Eye-detection-
Face and Eye detection using Cascades classifiers and opencv in python
Face-Recognition
Face Recognition (OpenCV, TransferLearning, TensorFlow, ...)
SezavarH's Repositories
SezavarH/Learned_Compression
In this notebook tutorial, we delve into the fascinating realm of learned data compression. With the advent of deep learning and neural networks, there's a new frontier in compression: learned data compression.
SezavarH/Transformers
Learning Transformers in detail
SezavarH/Event-based-data
Event-based data for compression
SezavarH/Face-Recognition
Face Recognition (OpenCV, TransferLearning, TensorFlow, ...)
SezavarH/Transfer-Learning
Advanced Transfer Learning with TensorFlow
SezavarH/Age-and-Gender-Estimation
Age and Gender Estimation
SezavarH/Bright-Spot-Detection
Bright Spot Detection using OpenCV
SezavarH/Face-and-Eye-detection-
Face and Eye detection using Cascades classifiers and opencv in python
SezavarH/Detecting-Parkinson-s-Disease--Machine-Learning.
This dataset is composed of a range of biomedical voice measurements from 31 people, 23 with Parkinson's disease (PD). Each column in the table is a particular voice measure, and each row corresponds to one of 195 voice recordings from these individuals ("name" column). The main aim of the data is to discriminate healthy people from those with PD, according to the "status" column which is set to 0 for healthy and 1 for PD.
SezavarH/ML_on_Heart_dataset
Machine Learning algorithms on Heart dataset
SezavarH/Task_scheduling
Implementaion of "Energy and Performance-Aware Task Scheduling in a Mobile Cloud Computing Environment"
SezavarH/data
Data and code behind the articles and graphics at FiveThirtyEight
SezavarH/code_snippets
SezavarH/Flask-SSLChatApp