How does TensorFlow handle gradient explosion in deep networks?

Gradient explosion is handled using clipping, normalization, and architectural constraints.

In deep networks, gradients can grow exponentially during backpropagation. TensorFlow mitigates this using gradient clipping (limiting gradient magnitude), normalization layers, and careful initialization strategies. Without this, training becomes unstable and loss diverges.