This course covers deep learning neural networks. Topics include logistic regression, feedforward networks, autoencoders, convolutional neural networks, recurrent neural networks, graph neural networks, deep generative models
DurationThis course covers deep learning neural networks. Topics include logistic regression, feedforward networks, autoencoders, convolutional neural networks, recurrent neural networks, graph neural networks, deep generative models, adversarial and reinforcement learning, and optimization and regularization techniques. Students also delve into recent research and learn through projects to develop deep learning systems.
A leader in interdisciplinary research and education
As the first school of its kind in the United States, the Indiana University Luddy School of Informatics, Computing, and Engineering is an innovator in a fast-paced and dynamic field. Our school on the IUPUI campus integrates computing, social science, and information systems design in unique ways.
More than 4,500 students—including over 1,400 at Luddy IUPUI—study informatics on IU campuses. Our top-notch programs and highly regarded faculty prepare them for the power and possibilities in computing and information technology.
Deep Learning algorithms aim to learn feature hierarchies with features at higher levels in the hierarchy formed by the composition of lower level features.
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