Pinned Repositories
AdvancedLaneFinding
The goal of the project is to detect lane boundaries for autonomous vehicle.
BehavioralCloning
The goal of this project is to build a convolution neural network trained on images collected from the car simulator to predict steering angles of the autonomous vehicle. The objective is to make sure that the model successfully drives our car around the virtual track without leaving the road.
CarND-Capstone
Udacity Self Driving Car ND - Capstone Project
compute-cassandra-python
Cassandra Cluster running on Google Compute Engine guideline
ExtendedKalmanFilter
The goal of the project is to estimate the state of a moving object of interest with noisy lidar and radar measurements utilizing the Kalman filter.
ParticleFilter
This project will implement a two dimensional particle filter in C++ to localize the car for autonomous driving. The particle filter will have access to the map, noisy initial localization information from GPS and noisy observation and control data from the sensors.
PathPlanning
Goal is to design a path planner that is able to create smooth, safe paths for the car to follow along a 3 lane highway with traffic. A successful path planner will be able to keep inside its lane, avoid hitting other cars, and pass slower moving traffic all by using localization, sensor fusion, and map data.
PIDController
PID controller for autonomous cars
SelfDrivingCar
The goal of the project is to apply Computer Vision and Deep Learning toward Perception problems like lane finding, classifying traffic signs, as well as a full end-to-end algorithm for driving with behavioral cloning. And also to track objects from radar and lidar data with sensor fusion and implement the Localization, Path Planning and Control to navigate and drive the car.
TrafficSignRecognition
The goal of the project is to build a CNN model for traffic sign recognition
iPhoring's Repositories
iPhoring/SelfDrivingCar
The goal of the project is to apply Computer Vision and Deep Learning toward Perception problems like lane finding, classifying traffic signs, as well as a full end-to-end algorithm for driving with behavioral cloning. And also to track objects from radar and lidar data with sensor fusion and implement the Localization, Path Planning and Control to navigate and drive the car.
iPhoring/CarND-Capstone
Udacity Self Driving Car ND - Capstone Project
iPhoring/ExtendedKalmanFilter
The goal of the project is to estimate the state of a moving object of interest with noisy lidar and radar measurements utilizing the Kalman filter.
iPhoring/PathPlanning
Goal is to design a path planner that is able to create smooth, safe paths for the car to follow along a 3 lane highway with traffic. A successful path planner will be able to keep inside its lane, avoid hitting other cars, and pass slower moving traffic all by using localization, sensor fusion, and map data.
iPhoring/PIDController
PID controller for autonomous cars
iPhoring/TrafficSignRecognition
The goal of the project is to build a CNN model for traffic sign recognition
iPhoring/AdvancedLaneFinding
The goal of the project is to detect lane boundaries for autonomous vehicle.
iPhoring/BehavioralCloning
The goal of this project is to build a convolution neural network trained on images collected from the car simulator to predict steering angles of the autonomous vehicle. The objective is to make sure that the model successfully drives our car around the virtual track without leaving the road.
iPhoring/compute-cassandra-python
Cassandra Cluster running on Google Compute Engine guideline
iPhoring/ParticleFilter
This project will implement a two dimensional particle filter in C++ to localize the car for autonomous driving. The particle filter will have access to the map, noisy initial localization information from GPS and noisy observation and control data from the sensors.
iPhoring/ComputerVision
Computer Vision - Drive
iPhoring/DataScience
Genetics and ML Related Artifacts
iPhoring/FindingLaneLines
When we drive, we use our eyes to decide where to go. The lines on the road that show us where the lanes act as our constant reference for where to steer the vehicle. Naturally, one of the first things we would like to do in developing a self-driving car is to detect lane lines using an algorithm automatically. In this project, I take a very first step to discover lane lines in images using Python and OpenCV.
iPhoring/ImageClassification
Objective: To assign a human identifiable label say alphabets, book, cat, human or dog etc. to an Image. If Computer Vision can attain human accuracy, then practical applications are limitless.
iPhoring/Pulitzer
As we all know that “Democracy Dies in Darkness1*” and “Truth is hard to Find2*” are the cornerstone of the economy. With this in mind, the goal is to identify different types of Newspapers Segments based on Pulitzer prize and then identify ways to increase daily circulations by improving visibility and gaining new insights. It may also act as a catalyst for further boosting readers confidence in the print media.
iPhoring/SomaticGermLine
The goal is to segment different types of Somatic Germline Mutations in human genes associated with inherited and acquired diseases. It will be a one-stop-shop comprehensive collection of mutation data(Segments) for easy discovery in the era of personalized medicine. As part of this project I would like to find: a. SomaticGermlinesegmentation b. AcquiredDiseasewithmaximumnumberofSomaticMutation c. InheritedDiseasewithmaximumnumberofSomaticMutation Outcome: Comprehensive Somatic Mutation Database an invaluable resource for all scientists.
iPhoring/training-data-analyst
Labs and demos for courses in the Data Engineer track of GCP Training (http://cloud.google.com/training).