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CS 7150 - Deep Learning |
Introduces deep learning, including the statistical learning framework, empirical risk minimization, loss function selection, fully connected layers, convolutional layers, pooling layers, batch normalization, multilayer perceptrons, convolutional neural networks, autoencoders, U-nets, residual networks, gradient descent, stochastic gradient descent, backpropagation, autograd, visualization of neural network features, robustness and adversarial examples, interpretability, continual learning, and applications in computer vision and natural language processing. Assumes students already have a basic knowledge of machine learning, optimization, linear algebra, and statistics.
4.000 Credit hours 4.000 Lecture hours Levels: Graduate Schedule Types: Lecture Computer Science Department Course Attributes: GSCS Computer & Info Science Restrictions: Must be enrolled in one of the following Levels: Graduate Must be enrolled in one of the following Colleges: Khoury Coll of Comp Sciences Prerequisites: Graduate level CS 6140 Minimum Grade of C- or Graduate level DS 5220 Minimum Grade of C- |
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