How can I implement model compression in my AI project?

How can I implement model compression in my AI project?

Model compression can be achieved through techniques like pruning, quantization, and knowledge distillation. Which one are you interested in?

Can you explain pruning and how it works?

Pruning involves removing weights from a neural network that contribute little to its output, reducing its size and improving efficiency.

What about quantization? How does it differ from pruning?

Quantization reduces the precision of the weights, converting them from floating-point to lower-bit representations, which saves memory and speeds up inference.

Are there any tools or libraries that can help with these techniques?

Yes, libraries like TensorFlow Model Optimization Toolkit and PyTorch provide built-in support for both pruning and quantization.