Deep Learning has gained a lot of popularity due to its recent breakthrough results in
many real-world applications such as speech recognition, machine translation, image
understanding, and robotics. The primary idea of deep learning is to build high-level
abstractions of the data through multi-layered architectures. This course introduces the
fundamental principles, algorithms and applications of deep learning. It will provide an indepth
understanding of various concepts and popular techniques in deep learning. This
course is mainly designed for beginning graduate students who are interested in studying
deep learning techniques and their practical applications.

The course begins with a thorough treatment of deep feedforward networks along with
various regularization and optimization techniques used for efficiently learning these
models. Different forms of the network architectures such as convolutional networks,
recurrent neural networks and autoencoders will be discussed in detail. Other advanced
concepts such as deep generative models and deep reinforcement learning will also be
covered. Finally, the course will conclude with a discussion on few real-world application
domains where deep learning techniques have produced astonishing results.