PyTorch
Machine Learning
A technique for reducing model size by removing unnecessary connections or neurons. This process identifies and eliminates redundant or less important parameters.
Detailed Explanation
Pruning in machine learning involves systematically removing unnecessary connections or neurons from a neural network to simplify the model. By eliminating redundant or less important parameters, pruning reduces complexity, helps prevent overfitting, and enhances computational efficiency, enabling faster inference and lower memory usage without significantly compromising the model’s accuracy.
Use Cases
- •Use case: Streamlining neural networks for deployment on edge devices to improve speed and reduce memory requirements.
Related Terms
Other terms in the Machine Learning category