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Related Questions
- What are the key differences between ReLU, Sigmoid, and Tanh activation functions in terms of their output ranges and gradients?
- How does the choice of activation function impact the model's ability to learn complex, non-linear relationships in deep neural networks?
- Can you explain the concept of vanishing gradients and how it relates to the choice of activation function in deep neural networks?
- How does the choice of activation function affect the model's ability to learn non-linear relationships in the presence of noise or outliers?
- What are some common techniques used to address the limitations of traditional activation functions, such as ReLU, in deep neural networks?
- Can you discuss the role of activation functions in convolutional neural networks (CNNs) and how they impact the model's ability to learn spatial hierarchies?
- How does the choice of activation function impact the model's ability to learn temporal relationships in recurrent neural networks (RNNs) and long short-term memory (LSTM) networks?
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