# Coursera: Machine Learning (Week 4) Quiz - Neural Networks: Representation| Andrew NG

# ▸ Neural Networks - Representation :

- Which of the following statements are true? Check all that apply.
- Any logical function over binary-valued (0 or 1) inputs x1 and x2 can be (approximately) represented using some neural network.
- Suppose you have a multi-class classification problem with three classes, trained with a 3 layer network. Let be the activation of the first output unit, and similarly and . Then for any input x, it must be the case that .
- A two layer (one input layer, one output layer; no hidden layer) neural network can represent the XOR function.
- The activation values of the hidden units in a neural network, with the sigmoid activation function applied at every layer, are always in the range (0, 1).

- Consider the following neural network which takes two binary-valued inputs

and outputs . Which of the following logical functions does it (approximately) compute?

- AND
This network outputs approximately 1 only when both inputs are 1.

- NAND (meaning “NOT AND”)
- OR
- XOR (exclusive OR)

- AND

- Consider the following neural network which takes two binary-valued inputs

and outputs . Which of the following logical functions does it (approximately) compute?

- AND
- NAND (meaning “NOT AND”)
- OR
This network outputs approximately 1 when atleast one input is 1.

- XOR (exclusive OR)

- Consider the neural network given below. Which of the following equations correctly computes the activation ? Note: is the sigmoid activation

function.

- You have the following neural network:

You’d like to compute the activations of the hidden layer . One way to do

so is the following Octave code:

You want to have a vectorized implementation of this (i.e., one that does not use for loops). Which of the following implementations correctly compute ? Check all

that apply.

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- You are using the neural network pictured below and have learned the parameters (used to compute ) and (used to compute as a function of ). Suppose you swap the parameters for the first hidden layer between its two units so and also swap the output layer so . How will this change the value of the output ?

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