Advanced Topics in Deep Learning
This is a seminar course in which the students read, present, and discuss research papers on deep learning. The focus will be mostly on applications in computer vision, but topics in natural language processing, language translation, and speech recognition will also be read and discussed. It is expected that students taking the course will have prior experience with deep learning and neural network architectures such as Convolutional Neural Nets (CNNs), Recurrent Neural Networks (RNNs), Long Short Term Memories (LSTMs), Gated Recurrent Units (GRUs), Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), etc. There will be no assignments other than reading and presentations. However, there will be a final project that students will work on throughout the duration of the semester. Enrollment is capped at 25 students. Instructor permission is required to register.
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