Model Capacity
The ability of a model to learn complex patterns, determined by its architecture and number of parameters.
In more detail
Model capacity affects the complexity of functions it can learn. Too little capacity causes underfitting; too much causes overfitting. Measured in parameters for neural networks. Balancing capacity with regularization is key to good generalization.
Examples
- Parameter count: GPT-3 (175B) vs GPT-2 (1.5B)
- Hidden layer sizes in neural networks
- Depth vs width tradeoffs
Related terms
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