What is quantization in machine learning?
Quantization in machine learning refers to the process of reducing the precision of the numbers used to represent model parameters.
Why is quantization important?
It's important because it helps to reduce the model size and improve inference speed, especially on resource-constrained devices.
How does quantization affect model accuracy?
Quantization can lead to a slight decrease in accuracy, but techniques like fine-tuning can help mitigate this loss.
What types of quantization are there?
There are several types, including uniform quantization, non-uniform quantization, and dynamic quantization.
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