What is the impact of activation functions on neural network performance?

What is the impact of activation functions on neural network performance?

Activation functions play a crucial role in determining how well a neural network learns. They introduce non-linearity, allowing the network to model complex relationships in data.

Can you explain the difference between ReLU and Sigmoid activation functions?

Sure! ReLU (Rectified Linear Unit) allows positive values to pass through while blocking negative ones, which helps with faster training. Sigmoid, on the other hand, squashes outputs to a range between 0 and 1, making it suitable for binary classification but can lead to vanishing gradients.

Which activation function is generally preferred for hidden layers?

ReLU is generally preferred for hidden layers due to its efficiency and ability to mitigate the vanishing gradient problem. However, variants like Leaky ReLU and ELU are also popular.

Are there any scenarios where using Sigmoid is beneficial?

Yes, Sigmoid can be beneficial in the output layer for binary classification tasks, as it provides a probability-like output.