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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.
Covers neuromorphic computing, challenges in ternary and binary computing, hardware simulations of the brain, and new materials for artificial brain cells.
Discusses the mean input shift and bias problem in weight updates for neural networks, highlighting the importance of correct initialization to prevent gradient issues.
