Weights and Biases
Weights and Biases are the fundamental learnable parameters within a neural network. Weights determine the strength of connection between nodes, while biases offset the activation output, allowing the network to shift activation curves.
Frequently Asked Questions
What happens to weights during training?▼
They are updated iteratively based on gradients computed during backpropagation, guided by the optimizer to minimize loss.
Why is initializing weights important?▼
Poor weight initialization can lead to training failure (vanishing/exploding gradients) or slow convergence.
Quick Facts
- CategoryMathematical Foundations
- Key ApplicationParameter updating, backpropagation gradient calculations, and model parameter weight sizing
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