Machine Learning Applied to Image Classification

Abstract

Machine Learning is a field of computer science with severe applications in the modern world. One of the main applications is the use of neural networks in computer vision, recognizing faces in a photo, analyzing x-rays, or identifying an artwork. This paper aims to explore the concepts of machine learning, supervised learning, and neural networks, applying the learned concepts in the CIFAR10 dataset, which is a problem of image classification, trying to build a neural network with high accuracy. To avoid overfitting we proposed trying to use two different methods of regularization: L2 and dropout. We find that using dropout regularization gives the best accuracy on our model when compared with the L2 regularization.