Neural Network-based Robot Navigation/ A.I.
October 2023, Arizona State University
• Developed a neural network model for a simulated robotics task, aiming to help a small robot navigate its environment without collisions.
• Collected and managed a dataset, designed a custom neural network architecture using PyTorch.
Handwritten Digits Recognition / Machine Learning
September 2023, Arizona State University
In this project, I implemented a machine learning model to recognize handwritten digits using the MNIST dataset, which contains 70,000 grayscale images of digits (0-9) labeled for classification. The dataset is widely used for benchmarking computer vision models. I preprocessed the images, applied feature extraction techniques, and trained a classification model using algorithms such as Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and Random Forests. The goal was to optimize accuracy while minimizing computational cost. This project helped me gain hands-on experience in deep learning, computer vision, and hyperparameter tuning to improve model performance.
Snippets of part of the project code:
