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Related Questions
- What are the different types of pruning techniques used in neural networks, such as weight pruning, neuron pruning, and filter pruning?
- How does weight pruning work, and what are its advantages and disadvantages?
- What is the difference between L1 and L2 regularization in neural networks, and how do they relate to pruning?
- Can you explain the concept of knowledge distillation and how it can be used in conjunction with pruning?
- What are some common metrics used to evaluate the effectiveness of pruning, such as accuracy, F1 score, and compression ratio?
- How can pruning be applied to different types of neural networks, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs)?
- What are some challenges and limitations of pruning, and how can they be addressed in practice?
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