SMRTR summary
Neural networks learn by adjusting two key values — weights, which control how much each input matters, and biases, which shift the output — through a process called gradient descent. Training in batches rather than one data point at a time keeps learning stable and prevents models from memorizing instead of generalizing.
SMRTR provides this summary for quick context. The original article belongs to Hacker Noon.
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