What is the impact of activation functions on training?
An example of an AI chatbot conversation about activation functions in neural networks.
What is the impact of activation functions on training neural networks?
Activation functions determine how the output of a neuron is calculated, affecting learning and performance.
Can you explain how different activation functions like ReLU and sigmoid affect training?
ReLU helps with faster training and reduces the likelihood of vanishing gradients, while sigmoid can cause saturation.
What are the common activation functions used in deep learning?
Common activation functions include ReLU, sigmoid, tanh, and softmax, each serving different purposes.
How do I choose the right activation function for my model?
Consider the type of problem, the architecture of your model, and experiment with different functions to see what works best.
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