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Key Principles & Architecture

Computer Science • CS401 • Alex Johnson • 2026-09-14

• Machine learning models learn patterns from input data without explicit rule programming.

• Neural Networks mimic biological neurons with interconnected layers: Input, Hidden, and Output.

• ReLU (Rectified Linear Unit): f(x) = max(0, x). Most popular for deep feedforward networks.

• Sigmoid: Maps values into [0, 1]. Best for binary classification output.

• Gradient descent iteratively adjusts synaptic weights to minimize the cross-entropy loss function.

Summary

Neural networks rely on forward propagation to compute loss and backpropagation to calculate gradients for gradient descent weight updates.

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