DL-ANN

The basic unit of an Artificial Neural Network is called:

• Kernel

• Perceptron

• Decision Tree

• Support Vector

The activation function in ANN is used to:

• Scale the input

• Normalize data

• Introduce non-linearity

• Measure accuracy

The purpose of using multiple hidden layers in deep learning is to:

• Increase model simplicity

• Reduce computation

• Eliminate overfitting

• Learn complex hierarchical patterns

The process of adjusting weights in ANN during training is called __________________
(answer in small case letters)

Which loss function is commonly used in binary classification with ANN?

• Cross-Entropy Loss

• Hinge Loss

• Gini Index

• Mean Squared Error

The number of neurons in the input layer of an ANN corresponds to:

• Number of classes

• Number of features

• Number of hidden layers

• Number of epochs

The main advantage of deep learning over traditional machine learning is:

• Does not require GPUs

• Always faster to train

• Automatically learns features

• Requires less data

ReLU activation function outputs:

• between 0 and 1

• Negative values only

• Max(0, x)

• Always binary outputs

  In deep learning, overfitting can be reduced by:

• Data Augmentation

• Adding more layers

• Increasing the learning rate

• Removing dropout layers

The learning rate in ANN controls:

• Step size during weight update

• Number of hidden layers

• Size of the dataset

• Number of neurons