Quiz on Multi-Layer Perceptrons and Optimization

What does MLP stand for in neural networks?

• Machine Learning Perceptron

• Multi-Layer Process

• Multi-Layer Perceptron

• Machine Learning Process

Which of the following is a characteristic of Multi-Layer Perceptrons?

• They use only linear functions

• They are feedforward neural networks

• They consist of a single layer

• They have feedback connections

What is the purpose of the activation function in a neural network?

• To initialize weights

• To introduce non-linearity

• To perform affine transformations

• To calculate the loss

What does the term 'global minima' refer to in optimization?

• The highest point in a function

• The lowest point in a convex optimization problem

• A point where the function is undefined

• A local minimum

What is the main goal of the least-squares algorithm?

• To adjust the activation functions

• To find the global maximum

• To maximize the error

• To minimize the cumulative error

In the context of ADALINE, what does the term 'gradient descent' refer to?

• A method for weight updates

• A type of activation function

• A loss function

• A neural network architecture

What is the formula for the linear activation function in ADALINE?

• g(x) = 1/x

• g(x) = x^2

• g(x) = e^x

• g(x) = x

What is the purpose of the Hessian matrix in optimization?

• To perform backpropagation

• To initialize weights

• To identify local maxima and minima

• To calculate the gradient

What happens when the gradient is zero at a point in a function?

• It is a local maximum

• It is a local minimum

• It is an inflection point

• It is a turning point

Which of the following describes a convex optimization problem?

• It has multiple local minima

• It has a unique global minimum

• It is always non-convex

• It cannot be solved using gradient descent