In this course, students will gain an understanding of the fundamentals in deep learning and then apply those concepts in exercises and applications in python. We'll start with the origins of artificial neural networks, learn about loss functions, understand gradient descent, back propagation and various training optimization techniques. Students will be familiar with canonical network architecture such as multi-layer perceptions, convolutional neural networks, recursive neural networks, LSTMs and GRU, attention and transformers. Through explanations, examples and exercises students will build intuition on how deep learning algorithms work and how they are implemented in popular deep learning frameworks such as PyTorch. Students will be able to define, train and evaluate deep learning models as well as adapt deep learning frameworks to new functionality. Students will gain exposure to pre-trained large language models and other foundation models and the concepts of few-shot learning and reasoning. Finally, students will be able to apply many of the techniques they learned in a final class project.

Note that this information may change at any time. Please visit the MyBU Student Portal for the most up-to-date course information.