ANN AND CNN

In an ANN, the function that introduces non-linearity is called the (a)   .

The process of adjusting weights based on error gradients is called (a)   .

The (a)   layer in an ANN receives raw input features.

Overfitting in ANN can be reduced using a technique called (a)   , where random neurons are ignored during training.

The universal function approximation theorem states that an ANN with at least one hidden layer can approximate any (a)   functions.

The (a)   algorithm is a variant of gradient descent that includes momentum and adaptive learning rates.

In an ANN, the sum of weighted inputs plus bias is passed through an (a)   to produce the output of a neuron.

A neural network without hidden layers is equivalent to performing (a)   regression.

The (a)   function is commonly used for binary classification problems in ANN.

In ANN, the weights are updated using the rule: New Weight = Old Weight – (a)   × Gradient.