Pinned Repositories
A-Star-implementation-and-simulation
Simulation Performed with different obstacle spaces as well as implemented A* on Turtle bot 2.
agile_robotics_industrial_automation
The objective of the Agile Robotics for Industrial Automation Competition (ARIAC) is to test the agility of industrial robot systems, with the goal of enabling industrial robots on the shop floors to be more productive, more autonomous, and be more responsive to the needs of shop floor workers.
agile_robotics_industrial_automation-1
awesome-courses
:books: List of awesome university courses for learning Computer Science!
awesome-deep-learning
A curated list of awesome Deep Learning tutorials, projects and communities.
awesome-robotics
A list of awesome Robotics resources
Comparison-and-Implementation-of-PRM-and-Lazy-PRM-on-mobile-robots
This paper describes a new approach to probabilistic roadmap planners (PRMs). The overall theme of the algorithm called Lazy PRM, whose aim is to minimize the number of collision checks performed during the planning and hence minimize the running time of the planner.
Machine-Learning-Coursera-AndrewNg
Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI. This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
Perception-for-Autonomous-Robots
Lane and vehicle alignment detection using Hough Lines/Probabilistic Hough lines, RANSAC, Traffic Sign detection and recognition using HOG + SVM for detection and recognition, Visual odometry.
Pick-and-Place-on-Baxter-using-ROS-and-V-REP
gkkhut's Repositories
gkkhut/Comparison-and-Implementation-of-PRM-and-Lazy-PRM-on-mobile-robots
This paper describes a new approach to probabilistic roadmap planners (PRMs). The overall theme of the algorithm called Lazy PRM, whose aim is to minimize the number of collision checks performed during the planning and hence minimize the running time of the planner.
gkkhut/Machine-Learning-Coursera-AndrewNg
Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI. This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
gkkhut/Perception-for-Autonomous-Robots
Lane and vehicle alignment detection using Hough Lines/Probabilistic Hough lines, RANSAC, Traffic Sign detection and recognition using HOG + SVM for detection and recognition, Visual odometry.
gkkhut/Pick-and-Place-on-Baxter-using-ROS-and-V-REP
gkkhut/A-Star-implementation-and-simulation
Simulation Performed with different obstacle spaces as well as implemented A* on Turtle bot 2.
gkkhut/agile_robotics_industrial_automation
The objective of the Agile Robotics for Industrial Automation Competition (ARIAC) is to test the agility of industrial robot systems, with the goal of enabling industrial robots on the shop floors to be more productive, more autonomous, and be more responsive to the needs of shop floor workers.
gkkhut/agile_robotics_industrial_automation-1
gkkhut/awesome-courses
:books: List of awesome university courses for learning Computer Science!
gkkhut/awesome-deep-learning
A curated list of awesome Deep Learning tutorials, projects and communities.
gkkhut/awesome-robotics
A list of awesome Robotics resources
gkkhut/Breadth-First-Search-on-given-Obstacle-Space
gkkhut/Cerebellar-Model-Arithmetic-Computer--CMAC
gkkhut/Coding_challenges
gkkhut/Deep-Reinforcement-Learning-for-Simulated-Self-Driving-Car
Deep Reinforcement Learning for Simulated Self Driving Car
gkkhut/Fundamentals-of-AI-and-DL-Framework
ENPM 809K
gkkhut/Game-of-Dots-and-Boxes
Game of Dots and Boxes using Q learning
gkkhut/Generating-all-nodes-with-the-solution-for-8-puzzle
Implementation of tree generation algorithm to generate nodes for 8-Puzzle and search algorithm to find the solution for any given state.
gkkhut/gkkhut.github.io
My Website
gkkhut/GPS-based-Vault-with-Integrated-Fingerprint-Scanner-and-Keypad-Lock
GPS-based-Vault-with-Integrated-Fingerprint-Scanner-and-Keypad-Lock
gkkhut/Northrop-Grumman-Image-Recognition-Challenge
Northrop Grumman Image Recognition Challenge at University of Maryland, College Park
gkkhut/PythonRobotics
Python sample codes for robotics algorithms.
gkkhut/Udacity-Data-Analyst-Nanodegree
Repository for the projects needed to complete the Data Analyst Nanodegree.
gkkhut/Udacity_DeepLearning_Nanodegree_Projects
gkkhut/Wireless-and-Mobile-systems-for-IoT