📌 Neural Network Pruning Summary
Neural network pruning is a technique used to reduce the size and complexity of artificial neural networks by removing unnecessary or less important connections, neurons, or layers. This process helps make models smaller and faster without significantly affecting their accuracy. Pruning often follows the training of a large model, where the least useful parts are identified and removed to optimise performance and efficiency.
🙋🏻♂️ Explain Neural Network Pruning Simply
Imagine a large tree with many branches, but only some branches are strong and needed for the tree to stay healthy. Pruning is like cutting away the weak or extra branches so the tree can grow better and use its energy more efficiently. In neural networks, pruning means cutting out the parts that do not help much, so the system can work faster and use less memory.
📅 How Can it be used?
Neural network pruning can be used to speed up an image recognition app so it runs efficiently on mobile devices.
🗺️ Real World Examples
A smartphone manufacturer wants their voice assistant to respond quickly without draining the battery. By pruning the neural network used for speech recognition, they make the model smaller and faster, allowing the assistant to run smoothly on the phone itself instead of relying on cloud servers.
A healthcare company uses neural network pruning to deploy a medical image analysis tool on portable scanners in remote clinics. The pruned model can analyse images rapidly on devices with limited computing power, helping staff diagnose conditions without needing constant internet access.
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