What is quantization in machine learning?

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, which can help in reducing model size and improving inference speed.

Why is quantization important?

Quantization is important because it allows models to run more efficiently on devices with limited computational resources, such as mobile phones and IoT devices.

How does quantization affect model accuracy?

Quantization can lead to a slight decrease in model accuracy, but with proper techniques, the impact can be minimized, making it a worthwhile trade-off for efficiency.

What are the different types of quantization?

The main types of quantization are uniform quantization, non-uniform quantization, and dynamic quantization, each with its own use cases and benefits.

Can you give an example of quantization in practice?

Sure! For instance, TensorFlow Lite uses quantization to convert a floating-point model into an integer model, which can significantly reduce the model size and improve performance on edge devices.