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$35 USD / hour
Flag of EGYPT
cairo, egypt
$35 USD / hour
It's currently 6:38 AM here
Joined May 5, 2016
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Ayman H.

@AymanHassan32

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$35 USD / hour
Flag of EGYPT
cairo, egypt
$35 USD / hour
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Software Developer,Data Scientist,Graphic Designer

Senior year Computer science student with multiple projects in different CS branches Skills: • Software Development • Machine Learning & Deep Learning( • Python (Tensorflow, Keras, OpenCV) • Java, C#, C++ • Git & Github Projects (Available at [login to view URL]): • ASL Sign Language Recognizer : Real-time hand sign recognizer using Deep Learning and Image Processing. • ML Projects (available on Kaggle): Titanic Dataset, MINST Fashion Dataset, Uber-Lyft Dataset, Breast Cancer Dataset • Khartoosha: A platformer game using Java & Lib-Gdx • Intelligent Scissor : A magnetic lasso tool for cropping images using C# • Pong Game : An Improved Version of the famous Pong Game • Facebook Scraper: Python Bot for scraping Facebook posts, likes, comments, etc. using Python, Selenium and BeautifulSoup Certificates : • [login to view URL]’s Neural Networks and Deep Learning course. • [login to view URL]’s Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization. • DataCamp’s Introduction to Natural Language Processing • Kaggle’s Python and Data Visualization Courses. • ECPC 2020 Qualification Round (25th place among 130 teams) • Google Hash Code 2020 Qualification Round (13th place on Ain Shams University) Github : [login to view URL] LinkedIn : [login to view URL]

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Portfolio

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Experience

HPC Trainee

Applied Innovation Center
Mar 2023 - Present
● Researched and applied solutions to monitor the HPC Cluster’s distributed storage system

Qualifications

Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

Coursera
2022
Learn the best practices to train and develop test sets and analyze bias/variance for building deep learning applications; be able to use standard neural network techniques such as initialization, L2 and dropout regularization, hyperparameter tuning, batch normalization, and gradient checking; implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence; and implement a neural network in TensorFlow.

Neural Networks and Deep Learning

Coursera
2021
Understand and Apply fully connected deep neural networks; implement efficient (vectorized) neural networks; identify key parameters in a neural network’s architecture; and apply deep learning to your own applications. Verify : https://coursera.org/verify/4593ZLQHNPE4

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