Deep Learning Course Outline: From Perceptrons to Transformers

This course outline covers a comprehensive range of deep learning topics, starting from early perceptrons and backpropagation algorithms, and progressing to modern Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformer models. The course will progressively explain techniques for training neural networks, including optimization algorithms and regularization methods. Advanced topics such as time series prediction, sequence-to-sequence prediction, and Generative Adversarial Networks (GANs) will also be covered. The course will be assessed through a series of lectures, assignments, and quizzes.