Deep Learning (BCS714A) - This course  introduces the fundamental concepts, architectures, algorithms, and applications of deep learning. The course begins with the foundations of neural networks, biological and machine vision, CNNs, and Natural Language Processing. It covers important models and concepts such as LeNet-5, AlexNet, word vectors, word2vec, regularization, optimization, convolutional neural networks, recurrent neural networks, LSTM, and sequence-to-sequence models.

The course also emphasizes practical and real-world applications of deep learning, particularly in image and language processing. Students learn to design and analyze deep learning models, apply regularization and optimization techniques, work with CNN and RNN architectures, and develop interactive NLP applications. Programming assignments, group activities, and exposure to recent research help students develop analytical, problem-solving, programming, and practical skills required to apply deep learning techniques to real-world problems.