Northwestern University 1 credit Computer Science
Deep learning is a branch of machine learning based on algorithms that try to model high-level abstract representations of data by using multiple processing layers with complex structures. One of the most exciting areas of research in deep learning is that of generative models. Today’s generative models create text documents, write songs, make paintings and videos, and generate speech. This course is dedicated to understanding the inner workings of the technologies that underlie these advances. Students will learn about key methodologies, including Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Transformer-based language models. This is an advanced course that presumes a good working understanding of traditional supervised neural network technology and techniques (e.g. convolutional networks, LSTMs, loss functions, regularization, gradient descent). Prerequisite: CS PhD students or Permission of Instructor.
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