Mohamed Ameen's repositories
CS-7641-Machine-Learning-Notes
In this repository, I will publish my notes for GaTech's Machine Learning course CS7641.
CS-6210-Advanced-Operating-Systems-Notes
In this repository, I will publish my notes for GaTech's Advanced Operating Systems course (CS6210).
German-Traffic-Sign-Classification-Using-TensorFlow
In this project, I used Python and TensorFlow to classify traffic signs. Dataset used: German Traffic Sign Dataset. This dataset has more than 50,000 images of 43 classes. I was able to reach a +99% validation accuracy, and a 97.3% testing accuracy.
Lane-lines-detection-using-Python-and-OpenCV
In this project, I used Python and OpenCV to detect lane lines on the road. I developed a processing pipeline that works on a series of individual images, and applied the result to a video stream.
CS-7642-Reinforcement-Learning-Notes
In this repository, I will publish my notes for GaTech's Reinforcement Learning course CS7642.
Advanced-Lane-Finding-Using-OpenCV
In this project, I used OpenCV to write a software pipeline to identify the lane boundaries in a video from a front-facing camera on a car.
An-Autonomous-Vehicle-System-For-Carla
In this project, we built ROS nodes to implement the core functionality of the autonomous vehicle system, including traffic light detection and classification, vehicle control control, and waypoint following.
Behavioral-Cloning-End-to-End-Learning-for-Self-Driving-Cars
In this project, I used a deep neural network (built with Keras) to clone car driving behavior. The dataset used to train the network is generated from Udacity's Self-Driving Car Simulator, and it consists of images taken from three different camera angles (Center - Left - Right), in addition to the steering angle, throttle, brake, and speed during each frame. The network is based on NVIDIA's paper End to End Learning for Self-Driving Cars, which has been proven to work in this problem domain.
Vehicle-Path-Planning-Algorithm
In this project, our goal is to design a path planning algorithm that is able to a car around a simulated highway scenario, including traffic and given waypoints, telemetry, and sensor fusion data.
Vehicle-Steering-Using-PID-Control
In this project, we implement a PID controller to steer the self driving car around the track in Udacity's Simulator.
Semantic-Segmentation-using-Fully-Convolutional-Networks
In this project, we'll construct a fully convolutional neural network based on the VGGNet-16 architecture to perform semantic segmentation on a video captured from a front facing camera mounted on a vehicle dashboard to identify the drivable surface area.
Vehicle-Detection-and-Tracking
In this project, I built a software pipeline to detect vehicles in a video.
Vehicle-Steering-Using-Model-Predictive-Control
The main goal of the project is to implement in C++ Model Predictive Control to drive the vehicle around the simulator track.
Vehicle-Localization-Using-Particle-Filters
In this project, we will implement a 2-dimensional particle filter in C++. The particle filter will be given a map and some initial localization information (analogous to what a GPS would provide) to localize a vehicle.
coursera-ml
Coursera Machine Learning Assignments
A-Benchmark-of-Supervised-Learning-Algorithms
A Benchmark of Supervised Learning Algorithms
kaggle_titanic
Predicting Titanic survival probability using several classification models
Sensor-Fusion-Using-Extended-Kalman-Filter
In this project we will utilize a kalman filter to estimate the state of a moving object of interest with noisy LIDAR and radar measurements.
Sensor-Fusion-Using-Unscented-Kalman-Filter
In this project we will utilize an unscented filter to estimate the state of a moving object of interest with noisy LIDAR and radar measurements.