Rutgers University 3 credits 544
This course covers basic techniques for neural networks and deep learning, sequential dense, convolutional, recurrent, autoencoders, variational autoencoders, Boltzmann machines, generative adversarial networks. The backpropagation algorithm and various optimization techniques for neural networks are covered. Applications in regression, classification, feature extraction, and generative models and natural language and pattern recognition are covered. Software libraries such as Tensorflow, PyTorch, Keras are extensively used. Available hardware on public cloud computing platforms are introduced and used. Students will participate in a substantial group semester project.
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