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2 Wᴏʀʟᴅ Rᴇᴄᴏʀᴅs Sᴇᴛᴛᴇʀ. Pʀᴏᴍɪsɪɴɢ Sᴄʜᴏʟᴀʀ & Pʀᴏᴠᴏsᴛ Sᴄʜᴏʟᴀʀ ᴀᴛ Gᴇᴏʀɢɪᴀ Tᴇᴄʜ.
The project uses a basic neural network to predict bike-sharing data on any given day given a history of previous bike-sharing data.
In this project, we generate The Simpsons TV script using previous examples. We use a recurrent neural network on 27 seasons of The Simpsons dataset of scripts.
This project used a convolutional neural network to interpret pictures of both humans and dogs to predict dog breeds and breeds of dogs closest to human faces.
In this project, I use a Deep Convolutional Generative Adversarial Network (and 2 neural nets) to generate new faces based on the CelebA face dataset.
In this project, my Pacman agent will find paths through his maze world, both to reach a particular location and to collect food efficiently.
The simple 32-bit data path is capable of performing advanced computational tasks and logical decision making. Now it is time to implement the ability for programs to be interrupted.
This project is designed to give a good feel for exactly how a processor works.
In this project, I will implement value iteration and Q-learning. I will test my agents first on Gridworld, then apply them to a simulated robot controller (Crawler) and Pacman.
By focusing on increasing throughput, a pipelined processor can get more instructions done per clock cycle. In the real world, that means higher performance, lower power draw, and happy customers!
In this project, I will be implementing a virtual memory system simulator. In most modern operating systems, user programs access memory using virtual addresses.
The Challenge: Using game play-by-play score sheet data from NCAAs website, build an opponent scouting report.
In this project, I will implement a multiprocessor operating system simulator using a popular userspace threading library for linux called pthreads.
Pacman spends his life running from ghosts, but things were not always so. Legend has it that many years ago, Pacman's great grandfather Grandpac learned to hunt ghosts for sport.
Created a Pacman agent with various algorithms including graph search traversal, Markov decision processes, and particle filtering.
In this project, I will be implementing neural nets, and in particular the most common algorithm for learning the correct weights for a neural net from examples.
In this project, my team and I created a Mass Transit Simulation app to be used by employees of MARTA (Metropolitan Atlanta Rapid Transit Authority).
The objective of this project is to use image processing and machine learning tools to correctly classify images.
The objective of this project is to implement a Particle Filter (a.k.a. Monte Carlo Localization).
In this project, I will implement two algorithms for clustering, namely the KMeans and Gaussian Mixture Model (GMM).
In this project, I am writing a Finite State Machine.
In this project, I draw SVG elements on a webpage and build visualizations from them.
In this project, I will enable Cozmo to localize within its arena using a Particle Filter.
I use Principal Component Analysis (PCA) to perform image compression, trying to find the most important pixels to keep while incrementally removing details.
For this project, I create an interactive visualization. Specifically, I create a filtering mechanism.
