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Explains the learning process in multi-layer neural networks, including back-propagation, activation functions, weights update, and error backpropagation.
Covers the fundamentals of multilayer neural networks and deep learning, including back-propagation and network architectures like LeNet, AlexNet, and VGG-16.
Explores the history, models, training, convergence, and limitations of neural networks, including the backpropagation algorithm and universal approximation.
Covers photonic extreme learning machines and reservoir computing, focusing on their architectures, programming techniques, and applications in optical computing.