This course develops learners’ ability to design, train, and evaluate advanced neural systems. It covers neural-network foundations, backpropagation, optimization, regularization, convolutional networks, sequence models, attention, transformers, representation learning, multimodal learning, graph neural networks, transfer learning, and efficient inference. Learners apply these architectures to vision, language, temporal, recommendation, multimodal, and connected data problems.