Ken Power's Projects
Production repository for the all-new Advantage360 Professional using ZMK engine
Optical Flow and Deep Learning Use Cases
Create a neural network architecture to automatically generate captions from images.
Computer Vision and Deep Learning exercises from Udacity's Computer Vision Nanodegree
Combine computer vision techniques and deep learning architectures to build a facial keypoint detection system.
A set of Object Motion and Localization projects, focused on localising robots, including self-driving cars.
Combine knowledge of robot sensor measurements and movement to create a map of an environment from only sensor and motion data gathered by a robot, over time.
A set of projects that illustrate some basic concepts in Deep Learning.
Depth AI
Deep Reinforcement Learning: Continuous Control. Solve the Unity ML-Agents Reacher Environment.
Examples and tutorials that implement various algorithms in Deep Reinforcement Learning.
Use Deep Reinforcement Learning to train an agent to navigate in a large, square world and collect bananas.
Computer vision use cases for autonomous drones.
My solutions to Google's Foobar Challenge for coding data structures and algorithms. Includes my solution code, unit tests, background notes, design notes, and references.
Hello
Optimization for Machine Learning
Transformers in Machine Learning
Analysis of 3D Magnetic Resonance (MR) Images
Reference Implementations of P0267, the proposed 2D graphics API for ISO C++
Tensors and Dynamic neural networks in Python with strong GPU acceleration
A software pipeline to identify the lane boundaries from a video of a road.
Use convolutional neural networks (CNNs) to clone driving behavior and train a self-driving car to autonomously navigate a track.
Program a real Self-Driving Car by writing ROS nodes to implement core functionality of the autonomous vehicle system.
Implement an Extended Kalman Filter (EKF) and use the EFK with noisy LiDAR and RADAR measurements to estimate the state of a moving object of interest.
Implement a 2-dimensional particle filter in C++.
A Path Planner that creates smooth, safe trajectories for a self-driving car to follow, enabling the car to safely navigate around a virtual highway with other traffic.
Implement a PID controller to enable a self-driving car to manoeuvre around a track.
Build a Convolutional Neural Network (CNN) that recognizes traffic signs.