What is exploding gradient problem?

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Exploding Gradient Problem

The exploding gradient problem occurs in deep neural networks (especially recurrent neural networks, RNNs) when gradients become very large during backpropagation.

• In training, neural networks update weights using gradients.

• If gradients grow too large, the weight updates become unstable.

• This causes the model parameters to diverge instead of converging, making training fail.

Why Does It Happen?

1. Deep Networks – Gradients are multiplied many times across layers. In some cases, instead of shrinking, they grow exponentially.

2. Poor Initialization – Large weight values can cause unstable gradient calculations.

3. Unrolled RNNs – In recurrent networks, backpropagating through time can amplify errors step after step.

Consequences

• Training becomes unstable.

• Loss function oscillates wildly or becomes NaN.

• Model fails to learn meaningful patterns.

How to Fix It?

1. Gradient Clipping – Limit the size of gradients during backpropagation.

2. Proper Weight Initialization – Use methods like Xavier or He initialization.

3. Normalization Techniques – Batch normalization or layer normalization helps stabilize values.

4. Smaller Learning Rate – Prevents excessively large weight updates.

5. Alternative Architectures – For RNNs, use LSTM or GRU, which mitigate gradient problems.

✅ In summary:
The exploding gradient problem happens when gradients become too large, leading to unstable training and divergence. It is fixed using techniques like gradient clipping, careful initialization, normalization, and stable architectures.

Read more:

What is the vanishing gradient problem?

Explain learning rate in optimization.

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