Candere.com Data Science Intern Interview Questions and Answers

Q1. What is the difference between sigmoid and softmax activation function?
Ans. 
Sigmoid is used for binary classification while softmax is used for multi-class classification.
• Sigmoid function outputs values between 0 and 1, suitable for binary classification tasks.

• Softmax function outputs a probability distribution over multiple classes, summing up to 1.

• Sigmoid is used in the output layer for binary classification, while softmax is used for multi-class classification.

• Softmax is the generalization
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Q2. What is an activation function ?
Ans. 
An activation function is a mathematical function that determines the output of a neural network.
• Activation functions introduce non-linearity to the neural network, allowing it to learn complex patterns in the data.

• Common activation functions include sigmoid, tanh, ReLU, and softmax.

• The choice of activation function can impact the performance and training speed of the neural network.
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