This course introduces the pivotal components of deep learning. You learn how to build deep feedforward, convolutional, and recurrent neural networks. The neural networks are used to solve problems that include traditional classification, image classification, and sequence-dependent outcomes. Hyperparameter search methods are demonstrated to find an optimal set of hyperparameters for deep learning models. Lastly, transfer learning is covered because this field has shown promise in deep learning.