What are model weights?

How It Works:

Weights are numerical parameters inside a neural network that adjust during training to minimize prediction errors and encode learned patterns.

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Key Benefits:

• Expressivity: Captures relationships in data.
• Transferable knowledge: Fine-tune pretrained weights on new tasks.
• Compact representation: Thousands to billions of parameters.

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Real-World Use Cases:

• Transfer learning: Use ImageNet weights for new vision tasks.
• Speech models: Warm-start from pretrained speech recognition weights.