| Catalog Content |
This course introduces the principles and architectures of neural networks for modeling complex data patterns. Students learn about perceptrons, multi-layer networks, activation functions, backpropagation, and optimization techniques. The course covers practical implementation of feedforward, convolutional, and recurrent neural networks, with applications in classification, regression, and sequence prediction tasks. Hands-on exercises emphasize training, evaluation, and fine-tuning of models using large datasets, enabling students to apply neural networks effectively in real-world big data analytics and machine learning projects. |
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