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Related Questions
- What are the differences between commonly used activation functions in language models, such as ReLU, Sigmoid, and Tanh, and how do they affect the model's output?
- How do the parameters of activation functions, such as the learning rate and regularization, influence the bias and accuracy of language models?
- Can you explain the concept of vanishing gradients in the context of activation functions and how it impacts the training of deep neural networks?
- How do different initialization methods for activation function parameters affect the convergence and accuracy of language models?
- What is the impact of activation function saturation on the representation learning capabilities of language models?
- How do non-linear activation functions, such as ReLU and Leaky ReLU, compare to linear activation functions in terms of bias and accuracy in language models?
- Can you discuss the role of activation function choice in the context of language model architectures, such as LSTM and Transformers, and how it affects their performance?
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