Deep Learning

3.0 credits · Graduate

Students will learn deep neural network fundamentals, including, but not limited to, feed-forward neural networks, convolutional neural networks, network architectures, optimization methods, practical issues, recurrent neural networks, transformers, generative models, foundation models, current limitations of deep learning, and visualization techniques. We still study applications to problems in computer vision and to a lesser extent other domains such as natural language and audio processing.