MicroBooNE, a Liquid Argon Time Projection Chamber (LArTPC), consists of liquid argon suspended in an electric field with finely separated sense wires for collecting ionization electrons drifted through the argon. These detectors are capable of imaging charged particle tracks in high resolution. Such detailed information will allow LArTPCs to perform accurate particle identification and calorimetry making it the detector of choice for many current and future neutrino experiments. However, analyzing such images currently require algorithms to identify and assemble several hand designed features in an image to reconstruct a particle interaction. Deep Learning, a machine learning algorithm using convolutional neural networks (CNN), is the state-of-the-art technique in computer vision. Deep Learning has found a wide range of application ranging from automated human face recognition and real-time object detection for self-driving cars, to speech recognition and playing games. We have explored the use of Deep Learning for analyzing neutrino events in MicroBooNE. I will present our first results of applying Deep Learning techniques to analyze LArTPC images and detail the current performance for particle identification, and neutrino interaction identification and detection.