Which of the following correctly describes a feedforward neural network?

I All of its units, called neurons, in a hidden layer are connected to all of the units in a previous layer.
II Its goal is to approximate a function
𝑓
(
𝑋
)
of the input
𝑋
to get an output
𝑦
.
III Feedforward refers to the direction of the information flows through the function being evaluated from
𝑋
up to the output
𝑦
.
IV They have feedback connections where the output of a certain layer is fed back to itself, making them the best choice for modeling time series.

Question

Which of the following correctly describes a feedforward neural network?

I	All of its units, called neurons, in a hidden layer are connected to all of the units in a previous layer. 
II	Its goal is to approximate a function 
𝑓
(
𝑋
)
 of the input 
𝑋
 to get an output 
𝑦
.
III	Feedforward refers to the direction of the information flows through the function being evaluated from 
𝑋
 up to the output 
𝑦
.
IV	They have feedback connections where the output of a certain layer is fed back to itself, making them the best choice for modeling time series.

BlackTom AI · Accepted Answer

Here we examine the statements about feedforward neural networks one by one to understand what accurately characterizes them.
Statement I: 'All of its units, called neurons, in a hidden layer are connected to all of the units in a previous layer.' In a typical feedforward neural network, neurons in one layer are connected to neurons in the next layer, and when those connections are dense (fully connected), each neuron in the latter layer receives input from every neuron in the previous layer. So, this description aligns with the common fully connected (dense) inter-layer connections found in many feedforward architectures, and it is generally correct.
Statement II: 'Its goal is to approximate a function f(X) of the input X to get an output y.' Indeed, a primary objective of feedforward networks is to approximate a mapping from inputs to outputs, effectively learning a function f that transforms X into a predicted y. This is a core perspective on supervised feedforward models, making this statement correct.
Statement III: 'Feedforward refers to the direction of the information flows through the function being evaluated from X up to the output y.' The term 'feedforward' emphasizes that information flows forward through the network—from input neurons through hidden layers to the output layer, without cycles or backward paths in standard feedforward nets. This description captures the essential notion of direction, so it is correct.
Statement IV: 'They have feedback connections where the output of a certain layer is fed back to itself, making them the best choice for modeling time series.' This describes feedback or recurrent connections, which are characteristic of recurrent neural networks (RNNs), not standard feedforward networks. Feedforward nets do not include such feedback loops, so this statement is false for feedforward architectures.

Putting these assessments together, Statements I, II, and III describe feedforward networks accurately, while Statement IV does not. The combination I, II, and III provides a correct characterization of feedforward neural networks.

FINTECH 540.01.Fa25 Final Exam

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