Deep Learning

Deep learning uses artificial neural networks to create representations of data with multiple levels of abstraction. Deep learning usually refers to a set of algorithms and computational models that are composed of multiple processing layers. These methods have significantly improved the state-of-the-art in many domains including Natural Language Processing (NLP), Natural Language Understanding (NLU), Speech Recognition, Computer Vision, Classification, Pattern Recognition and Bioinformatics. This course will cover the modern practice of deep networks, different architectures of deep networks including feed forward and convolutional models, methods for sequence modeling, variational and adversarial models, attention mechanism and optimization and regularization for deep models.