Name: Mostafa Hussein
Type: User
Company: ZF Friedrichshafen AG
Bio: - Research&Development Engineer (Algorithms for Autonomous Driving)
- M.Sc. in Embedded Systems
- Research Interests: Autonomous Driving and Deep Learning
Location: Friedrichshafen, Germany
Blog: https://www.linkedin.com/in/mostafa-husseinsh/
Mostafa Hussein's Projects
Curated List of Self-Driving Cars and Autonomous Vehicles Resources
A curated list of awesome Deep Learning tutorials, projects and communities.
:metal: awesome-semantic-segmentation
Open-source simulator for autonomous driving research.
Advanced Lane Finding Project for Self-Driving Car ND
Behavioral Cloning Project for Self-Driving Car ND
Capstone Project for Self-Driving Car ND
Extended Kalman Filter Project for Self-Driving Car ND
Kidnapped Vehicle Project for Self-Driving Car ND
Lane Finding Project for Self-Driving Car ND
Path Planning Project for Self-Driving Car ND
PID Control Project for Self-Driving Car ND
Traffic Light Detection and Classifier Project for Self-Driving Car ND Capstone Project
Traffic Sign Classifier Project for Self-Driving Car ND
Code to accompany ICML 2018 paper
Extension of the public darknet repository with additional features and code improvements for YOLO.
Convolutional Neural Networks
Deep Learning Lab Course - University of Freiburg
Deep Learning Course - University of Freiburg
A curated list of Generative Adversarial Networks (GANs) resources sorted by reputation
GAN Lab: An Interactive, Visual Experimentation Tool for Generative Adversarial Networks
Models and examples built with TensorFlow
Code samples for my book "Neural networks for the determined"
Latex code for making neural networks diagrams
Image-to-image translation in PyTorch (e.g., horse2zebra, edges2cats, and more)
pytorch-deeplab for non-square input images and trained on gta5
Implementing a feed forward neural network (VGG Net) for detecting race car in a given track, and localizing it (x, y and psi)
Reinforcement Learning Course - University of Freiburg
The RoboCup Logistics League (RCLL) is a league of the annual international robotics competition RoboCup. It focuses on in-factory logistics applications. Following the RoboCup spirit this league’s objective is to enable scientific work in order to achieve a flexible solution of material and informational flow within industrial production using coordinated teams of autonomous mobile robots. The task of the robots is to fetch raw materials from an input storage, transport them in a dynamic sequences between machines, handling production at these machines, and finally deliver them. A team consists of three robots. Each robot builds on the standardized Festo Robotino robot platform which can be extended with sensors and computing devices.
Bachelor Thesis