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Covers the fundamentals of multilayer neural networks and deep learning, including back-propagation and network architectures like LeNet, AlexNet, and VGG-16.
Discusses assembling neural networks by defining space and populating it with neurons, emphasizing the challenges and strategies for accurate morphologies and volume information.
