How does TensorFlow ensure numerical stability in deep neural networks?

TensorFlow uses stable operations, normalization layers, and controlled initialization to prevent numerical instability.

Deep networks suffer from exploding or vanishing gradients. TensorFlow mitigates this using numerically stable ops (log-sum-exp), batch normalization, layer normalization, and careful weight initialization (Xavier/He). Without these, floating-point precision errors accumulate and break training.