Vanishing Gradient Problem
The Vanishing Gradient Problem is a training difficulty in deep neural networks where the gradients of the loss function shrink exponentially as they propagate backward to the early layers, preventing the model weights from updating and learning.
Frequently Asked Questions
What causes the vanishing gradient problem?▼
It is primarily caused by using activation functions like Sigmoid or Tanh, whose derivatives are less than 1. Multiplying many such small values together during backpropagation causes the gradient to decay to zero.
How do modern architectures mitigate the vanishing gradient problem?▼
By using ReLU activation functions (which do not saturate for positive values), implementing residual connections (skip connections) that allow gradients to flow directly, and using Layer Normalization.
Quick Facts
- CategoryModel Training
- Key ApplicationDesigning deep recurrent networks, choosing activation functions (like ReLU), and initializing weights.
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