Applications of Deep Neural Networks Course Overview (1.1, Fall 2021)

Fall 2021 Version. Applications of deep neural networks is a course offered in a hybrid format by Washington University in St. Louis. This course introduces Keras deep neural networks and highlights applications that neural networks are particularly adept at handling compared to previous machine learning models. Deep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to create neural networks that can handle tabular data, images, text, and audio as both input and output. Deep learning allows a neural network to learn hierarchies of information in a way that is like the function of the human brain. This course will introduce the student to classic neural network structures, Convolution Neural Networks (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Neural Networks (GRU), General Adversarial Networks (GAN), and reinforcement learning. Application of these architectures