Improving Marvel Hero Classification through Dataset Curation

Abstract

lores the development of a computer vision model for classifying Marvel superheroes such as Black Widow, Hulk, Iron Man, and Spider-Man. Utilizing a curated dataset sourced from Kaggle, the research emphasizes the critical role of dataset quality in refining model accuracy. Insights gained include adjustments to neural network configurations and leveraging Edge Impulse for enhanced performance. The findings highlight effective strategies for optimizing classification accuracy in complex image recognition tasks.PART 2:This part explores the application of Bayesian logistic regression to model the relationship between temperature and the probability of O-ring failure. By leveraging Bayesian inference techniques, analyzing historical data to quantify the risk associated with temperature variations and emphasize the importance of probabilistic approaches in safety-critical decision-making.The Space Shuttle Challenger disaster on January 28, 1986, remains a poignant case study in aerospace engineering failure. The investigation concluded that the failure of O-ring seals in cold temperatures led to the tragic loss of the shuttle and its crew.