This course provides foundational knowledge in Deep Learning, one of the highly demanded skills in AI. Application of Deep Learning algorithms transform fields such as computer vision, speech recognition, natural language processing, medical image analysis, drug design, audio recognition.

Students will be introduced to various state-of-the-art Neural Network architectures (eg DNNs, CNNs, RNNs, LSTMs) and techniques (eg Stochastic Gradient Descent, Dropout, Batch norm, Transfer Learning). Students will work on real-life datasets to implement techniques applicable in domains such as image recognition, autonomous driving, gaming, healthcare, fraud detection. Instructor-led discussion, along with reading, written, and practical assignments. Assessment via exams and projects.