List different activation neurons or functions

by
  1. Linear neuron
  2. Binary threshold neuron
  3. Stochastic binary neuron
  4. Sigmoid neuron
  5. Tanh function
  6. Rectified linear unit (ReLU)

In an artificial intelligence interview, when asked about different activation functions or neurons used in neural networks, you could provide a comprehensive list including:

  1. Sigmoid Function (Logistic): This function squashes the input values between 0 and 1, suitable for binary classification tasks.
  2. Hyperbolic Tangent (Tanh) Function: Similar to the sigmoid function but squashes the input values between -1 and 1, often used in hidden layers of neural networks.
  3. Rectified Linear Unit (ReLU): This activation function returns 0 for negative inputs and the input value for positive inputs, effectively introducing non-linearity in the network.
  4. Leaky ReLU: A variant of ReLU that allows a small, positive gradient for negative inputs, helping to mitigate the “dying ReLU” problem.
  5. Parametric ReLU (PReLU): An extension of Leaky ReLU where the slope of the negative part is learned during training.
  6. Exponential Linear Unit (ELU): Similar to ReLU but with a smooth curve for negative inputs, which can help speed up convergence and improve robustness.
  7. Scaled Exponential Linear Unit (SELU): A self-normalizing activation function that maintains a constant mean and variance of the inputs, often used in deep learning architectures.
  8. Softmax Function: Typically used in the output layer of a neural network for multi-class classification tasks, it converts raw scores into probabilities.
  9. Linear Activation: A simple identity function where the output is proportional to the input, often used in the output layer for regression tasks.
  10. Swish Function: Proposed as a self-gated activation function, it performs as a smoother alternative to ReLU.
  11. Gaussian Error Linear Units (GELUs): Introduces a non-monotonicity into the network, which can help in certain contexts.
  12. Maxout Units: Neurons that take the maximum activation from a set of linear functions of the input.
  13. Hard Tanh: A piecewise linear function that approximates the hyperbolic tangent function, with faster computations.
  14. Softplus: A smooth approximation of ReLU, with non-zero gradients for all inputs.
  15. Binary Step: Simple thresholding function where values above a certain threshold are set to 1 and values below to 0, mainly used in binary classification problems.

When discussing these activation functions, it’s important to consider their properties, advantages, and limitations in different contexts and tasks.