Neural Networks: A Broad Overview

Short summary

Neural networks learn by adjusting weights and biases to minimize loss through gradient descent. Activation functions like ReLU introduce non-linearity; without them, stacked layers collapse into a single linear transformation. The fundamental tension between bias (underfitting) and variance (overfitting) drives architecture design.

• •Neural networks fit data by adjusting weights, biases, and parameters iteratively to minimize loss
• •Activation functions like ReLU enable non-linear learning; without them, deep networks reduce to single linear transformations
• •The bias-variance tradeoff shapes architecture choices: balancing underfitting against overfitting

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