Mohamed Abdulaziz (@demmojo)
  • Stars
    star
    94
  • Global Rank 211,022 (Top 8 %)
  • Followers 29
  • Following 50
  • Registered almost 7 years ago
  • Most used languages
    Python
    55.6 %
    HTML
    22.2 %
    JavaScript
    11.1 %
    CSS
    11.1 %
  • Location 🇸🇰 Slovakia
  • Country Total Rank 229
  • Country Ranking
    Python
    26
    CSS
    31
    HTML
    96

Top repositories

1

lstm-electric-load-forecast

Electric load forecast using Long-Short-Term-Memory (LSTM) recurrent neural network
Python
69
star
2

colabrnn

Train a bidirectional or normal LSTM recurrent neural network to generate text on a free GPU using any dataset. Just upload your text file and click run!
Python
12
star
3

prove-it

Prove-it is an Ethereum decentralized application that allows users to prove the existence and integrity of some data such as documents, images or videos.
CSS
6
star
4

decentralized-market

In this project, I developed a decentralized market on the Ethereum platform where users can open stores, add items to the stores, just shop and more!
JavaScript
2
star
5

mohamedabdulaziz.com

This repository contains a stylish, easy-to-customize personal portfolio template created using HTML5, CSS and JavaScript. It is lightweight,fully responsive and loaded with Font Awesome.
HTML
1
star
6

ResampleAndInterpolate

ResampleAndInterpolate changes the sampling rate of original data from every 1 hour to every 10 seconds and then applies linear interpolation to the missing data.
Python
1
star
7

Day-of-the-Week-Calculator

Finds the day of the week on any date of the Gregorian calendar.
Python
1
star
8

poetry-generator-app

A text generator web app using a pre-trained recurrent neural network model. The Python application is served using a uWSGI application server and Nginx as the front-end reverse proxy. Check website for a live demonstration:
HTML
1
star
9

text-rnn

text-rnn allows you to create modern neural network architectures which use modern techniques such as skip-embedding and attention weighting. Train either a bidirectional or normal LSTM recurrent neural network to generate text using any dataset. You can continue training a pre-trained model.
Python
1
star