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The Precision Trade-off

Smaller numbers are faster, but they can't always tell the truth.

Key Insight​

A tensor's dtype determines its memory size and accuracy. Low-precision types (like float16) save space but can "round off" important small values to zero.

Why This Matters​

In deep learning, small values (like gradients) are everything. Choosing the wrong precision can cause your model to stop learning or explode with "NaN" errors.