This course introduces the fundamental concepts, architectures, and applications of artificial neural networks (ANNs). It covers both theoretical and practical aspects, with a focus on the biological inspiration, learning algorithms, and implementation techniques of ANNs. Key models include perceptrons, multi-layer networks, backpropagation, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). Students will also gain hands-on experience developing neural network models for
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