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carnd-capstone-1

Programming a Real Self-Driving Car: In this project ROS nodes are developed to implement the core functionality of an autonomous vehicle system, including traffic light detection, control, and waypoint following. The system is first tested on a simulator and then on a real car. The solution is implemented using ROS, C++, Python and TensorFlow.

cimg-python

The Next-Gen CircleCI Python Docker Convenience Image.

deep-learning-v2-pytorch

Projects and exercises for the latest Deep Learning ND program https://www.udacity.com/course/deep-learning-nanodegree--nd101

dotfiles

face-recognition

Android application for face recognition

freak-on-fpga

This project was developed at Laboratorio de Computación Reconfigurable (LCR) - Universidad Tecnológica Nacional, Mendoza, Argentina. It allows to simulate and synthetize a hardware architecture for extraction of descriptors, based on the FREAK (Fast Retina Keypoint) algorithm. The arquitecture is described using VHDL, ModelSim and ISE Xilinx tools.

hub

Fastest unstructured dataset management for TensorFlow/PyTorch. Stream data real-time & version-control it. http://activeloop.ai

parallelfiresimulator

This project consists in the parallelization of a fire simulator. The followed strategy is a master-workers combined with multithread computation inside each node. The parallelization consists on testing every possible combination of parameters during simulation (model, wind velocity, etc.). This work is the final project of the course "Parallel Computing", Department of Information Systems Engineering, National University of Technology

sdcn-advanced-lane-finding

In this project, techniques such as camera calibration, distortion correction, perspective, color and gradient transforms are used to detect lane lines on the road using Python and OpenCV.

sdcn-behavioral-cloning

In this project, a convolutional neural network is trained to output the steering angle given video input from a camera located in front of a car, so that to autonomously drive a car in a simulator. The model is trained, validated and tested using Keras in Python.

sdcn-extended-kalman-filter-project

In this project a Kalman filter is implemented in C++ to estimate the state of a moving object of interest with noisy LIDAR and RADAR measurements.

sdcn-finding-lane-lines

A basic detector of lane lines in images is implemented using Python and OpenCV.

sdcn-path-planning-project

In this project the goal is to safely navigate around a virtual highway with other traffic. The car tries to go as close as possible to the 50 MPH speed limit, which means passing slower traffic when possible. The car avoids hitting other cars at all cost as well as driving inside of the marked road lanes at all times, unless going from one lane to another. The solution is implemented in C++.

sdcn-pid-control-project

In this project a PID controller is implemented in C++ to maneuver a vehicle around a track

sdcn-traffic-sign-classifier

A convolutional neural network is trained to classify traffic signs. The model is trained, validated and tested using Tensorflow in Python.

sdcnd-kidnapped-vehicle-project

In this project a 2D particle filter is implemented in C++ for localization of a robot, based on an initial GPS position and sensor and control data.

star-wars

Sample project with flask

traffic-light-detection-tensorflow

Traffic Light Detection and Classification using TensorFlow

visual-search

Image similarity search in large datasets