One important area of current research is the use of deep neural networks to classify or forecast time-series data. Time-series data is produced in large volumes from sensors in a variety of application domains including Internet of Things (IoT), cyber security, data center management and medical patient care. In this lab, you will learn how to create training and testing datasets using electronic health reacords in HDF5 (hierarchical data format version five) and prepare datasets for use with recurrent neural networks (RNNs), which allows modeling of very complex data sequences. You will then construct a long-short term memory model (LSTM), a specifc RNN architecture, using the Keras library running on top of Theano to evaluate model performance against baseline data. Prerequisites: Basic understanding to CNNs and RNNs. This lab utilizes GPU resources in the cloud, you are required to bring your own laptop.