trashBinPoster.pdf
Gradient Descent
In this project, I simulated the process of optimizing a vehicle's suspension system using gradient descent algorithms with and without momentum. Given a function of two parameters k and c (representing spring stiffness and damping coefficient), the algorithm finds the optimal values of each parameter that minimizes the function, representing the suspension system with the least disturbance.
Fashion-MNIST
(Neural Network)
In this project, I designed a neural network capable of classifying images in the Fashion-MNIST dataset with a final accuracy of 85.7%. This project taught me all of the necessary aspects of creating, training and testing neural networks.
Nonlinear Regression
Application is no longer available.
Application is no longer available.
In this project, I compared the accuracy of Fourier and exponential basis functions in approximating the angle (theta 1) of a double pendulum system over time. I then compared the results of each basis function to understand why the accuracy varied between methods.
Application is no longer available.
Autoencoders
In this project, I simulated a common engineering problem of analyzing measured data to determine which part of a system is malfunctioning. Using data from two different suspension systems, I created, trained and used an auto encoder to embed the data into two separate clusters which I was then able to extract and plot. This allowed the faulty suspension system to easily be determined.