Neural Network Fundamentals
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1Data Preprocessing SetupConfigure your dataset with proper normalization and validation splits. This foundation determines model performance more than most people realize.
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2Architecture SelectionChoose layer types and activation functions based on your problem domain. Start simple – you can always add complexity later.
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3Training ConfigurationSet learning rates, batch sizes, and optimization parameters. These hyperparameters often make the difference between success and frustration.
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4Performance MonitoringImplement validation tracking and early stopping. Watching your model learn is both fascinating and essential for preventing overfitting.