MSAI 531 - Neural Networks and Deep Learning

Credit(s): 3 hours
This course explores the principles of neural networks, starting from the basic building blocks and progressing to complex architectures. The course topics include activation functions, feed-forward networks, backpropagation, gradient descent optimization, regularization techniques, and model evaluation. In addition, this course covers deep learning, convolutional neural networks (CNNs) for image analysis and recognition, recurrent neural networks (RNNs) for sequential data modeling, and attention mechanisms for tasks like language processing and machine translation.