📌 Model Compression Summary
Model compression is the process of making machine learning models smaller and faster without losing too much accuracy. This is done by reducing the number of parameters or simplifying the model’s structure. The goal is to make models easier to use on devices with limited memory or processing power, such as smartphones or embedded systems.
🙋🏻♂️ Explain Model Compression Simply
Imagine you have a huge, heavy textbook but you only need a small summary to remember the main points. Model compression is like creating that summary for a machine learning model, so it is easier to carry around and use. This means the model can still do its job well, but it takes up less space and works faster.
📅 How Can it be used?
Model compression can help deploy AI features on mobile apps where speed and storage are limited.
🗺️ Real World Examples
A company wants to use voice recognition on its smart speakers. By compressing the speech recognition model, the device can process commands locally without sending data to the cloud, making it faster and more private.
A healthcare provider uses compressed deep learning models on portable medical devices, enabling them to analyse patient data in real time during remote visits, even with limited hardware resources.
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