This course introduces the pivotal components of deep learning. You learn how to build deep feedforward, convolutional, and recurrent networks. Neural networks are used to solve problems that include traditional classification, image classification, and sequence-dependent outcomes. The course contains a healthy mix of theory and application. Hands-on demonstration and practice problems are included to reinforce key concepts. Hyperparameter search methods are described and demonstrated to find an optimal set of deep learning models. Lastly, transfer learning is covered because the emergence of this field has shown promise in deep learning.
