This course introduces students to one of the most popular and fast-growing fields of machine learning – deep learning. It aims to provide the students with an understanding of the underlying principles of modern neural networks, their construction and applications (including NLP and computer vision). It covers common network architectures including convolutional and recurrent networks, backpropagation, regular and variational autoencoders, embeddings and more. The course will grant students understanding of DL best practices, and DL hardware and environments, including providing familiarity with PyTorch.