Binghamton University CS
This course introduces deep learning. It will cover fundamentals of neural networks, including architecture, computation graphs, computation theories, backpropagation, and gradient descent. It will also cover the major deep learning architectures including multilayer perceptrons (MLP), convolutional neural networks (CNN), recurrent neural networks (RNNs). We will then cover more advanced architectures and methods such as transformers, generative DL, and graph neural networks. Coursework will include building and training deep learning architectures using popular packages such a NumPy and PyTorch. Prior background is not assumed. Semester offered varies.
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