Green leaf

Omar S - Numpy developer
Member since:Feb 10, 2022
Profile last updated:Jul 27, 2022
Last activity:Aug. 11, 2022, 10:39 p.m. UTC

Omar S

I'm interested in continuing to develop my skillset in machine learning and understanding the real-world applications of using big data to make robust predictions.

Skills:     ·    ·    ·    ·    ·    ·    ·    ·    · 
Weekly Availability: 20 hours
Sun 10 - 15 03 - 08
Mon 10 - 15 03 - 08
Wed 10 - 15 03 - 08
Thurs 10 - 15 03 - 08

Hourly Rate: $21.00
Experience: 2 yrs
Engineer's Devices:

LD Talent Work History

Average response time:
8.2 hours
Avg count of messages / Day:
Average client satisfaction:

Worksession approval:
# Hires / # Interviews :
1 / 3
# Projects in last 2 weeks:
# Hours worked in last 2 weeks:


LD Talent     Developer     Contract
Apr 2022 - Present

  • I created a background removal using computer vision.
  • I Implemented a grammar checker using natural language processing tools.

Skills used: Computer vision, Natural language processing

the American university in Cairo     music genre classification using deep neural networks     Student     Course Project
Sep 2021 - Dec 2021

  • used the librosa library to extract features from 1000 different songs
  • used pandas to perform standard scaler preprocessing to preprocess the data and clear it
  • numpy was used to implement neural network model
  • used scikit-learn to measure the recall, precision, accuracy and f1-score of my model
  • machine learning feature extraction techniques were used to make the learning process more smooth

Skills used: Machine learning, Numpy, Pandas, scikit-learn

Link to the project:
the American university in Cairo     Single-Image-Colorization-and-Super-Resolution     Student     Course Project
Jan 2021 - May 2021

  • The Super Resolution Part was done using using Tensorflow and keras to create two networks (Modified EDSR and DQE) one to upscale the image by 2x and the other is to make the image more natural.
  • For the Colorization part, a deep CNN was used with about 24M trainable parameters with the last layer predicting the AB channels of the LAB color scale. Then the AB predicted channels are joined.
  • Matplotlib was used to display the images.

Skills used: Matplotlib, Tensorflow, keras

Link to the github:
Benchmarq Labs     social network analysis     Software Engineer     Internship
Jan 2021 - Feb 2021

  • used pandas to preprocess the data and put it in the needed format
  • used networkx to construct a graph of the most prominent actors given a topic
  • used matplotlib to plot the several statistics about the actors
  • used numpy, the network and pandas to extract the position, salience and capability of the top 10 most influential actors in a topic

Skills used: Matplotlib, Pandas, Numpy

Link to the project:


The American University in Cairo    B.Sc  (computer engineering)
Aug 2017 - May 2022

Client Reviews

LD Talent:    
Making good progress. Takes feedback well. Overall looking forward to more work with him.