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

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

Activation functions determine how the weighted sum of inputs is transformed into an output in a neural network. They play a crucial role in introducing non-linearity, which allows the network to learn complex patterns.

Can you explain the different types of activation functions?

Sure! Common activation functions include Sigmoid, Tanh, ReLU, and Softmax. Each has its own characteristics and use cases. For example, ReLU is popular for hidden layers due to its simplicity and efficiency.

How do activation functions affect the convergence of the training process?

Activation functions can significantly influence convergence speed and stability. For instance, using ReLU can lead to faster convergence, while Sigmoid may cause vanishing gradients, slowing down training.

Are there any activation functions that are better for specific tasks?

Yes, for example, Softmax is often used in the output layer for multi-class classification tasks, while ReLU is preferred in hidden layers for deep networks.