Explain vanishing and exploding gradient problems.

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1. Vanishing Gradient Problem

• What it is: During backpropagation, gradients (error signals) become very small as they are multiplied through many layers.

• Result: Weights update very slowly → lower layers stop learning → training stalls.

• Cause: Activation functions like Sigmoid or Tanh squash outputs to small ranges, making derivatives very small (< 1). Repeated multiplication shrinks gradients toward zero.

• Effect:

  • Slow or no convergence.

  • Deep networks fail to learn long-term dependencies (common in RNNs).

2. Exploding Gradient Problem

• What it is: During backpropagation, gradients grow uncontrollably large.

• Result: Weights update too aggressively → unstable training → model diverges (loss becomes NaN or oscillates).

• Cause: Large weight values or activation functions with large derivatives cause gradients to blow up when multiplied across layers.

• Effect:

  • Model fails to converge.

  • Sudden spikes in loss.

Solutions

🔹 For Vanishing Gradients:

• Use ReLU, Leaky ReLU instead of Sigmoid/Tanh.

• Apply Batch Normalization.

• Use Residual Connections (ResNets).

• Careful weight initialization (Xavier, He initialization).

🔹 For Exploding Gradients:

• Apply Gradient Clipping (limit max gradient value).

• Use smaller learning rates.

• Proper weight initialization.

✅ In short:

• Vanishing gradients → network stops learning (gradients → 0).

• Exploding gradients → unstable training (gradients → ∞).
  Both problems are common in deep and recurrent networks, and modern techniques (ReLU, batch norm, gradient clipping, ResNets) help overcome them.

Read More :

What are activation functions, and why are they important?

What is dropout in neural networks?

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