A Neural Network is a statistical tool that aids in the classification of data points for machine learning. Autoencoders are a type of neural network that are capable of learning large amounts of data without being supervised by labeled training data. We use autoencoders to help with the curse of dimensionality, a common data science problem that involves an excess number of predicting variables. Autoencoders can build models while only using the most important information. In this presentation, we are going to look at three different types of autoencoders and analyze the results to see which performs better. The different methods we will explore are stacked, convolutional, and denoising. We will apply these autoencoders to an image classification problem, intending to achieve data compression.