Unlock: Linear Layer: Shapes, Bias, and Memory

A systems-first note on the linear layer: tensor shapes, the bias term, forward pass, backward pass, parameter memory, FLOPs, and finite-difference gradient tests.

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Metric Spaces, Convergence, and Completeness is your weakest prerequisite with available questions. You haven't been assessed on this topic yet.

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