Learn where to place algorithms that process signals in a perceptron neural network. Detailed explanation for the IBM Artificial Intelligence Fundamentals certification exam.
Table of Contents
Question
You are designing a perceptron. Where will you locate the algorithms that run on the signal and then next passes the results to the output layer?
A. In the input layer
B. In the hidden layers
C. In the neurotransmitter
D. In the nucleus
Answer
B. In the hidden layers
Explanation
You would place them in the perceptron’s hidden layers. A perceptron has an input layer, one or more hidden layers, and an output layer. A signal enters the input layer and the hidden layers run algorithms on the signal. Then, the result is passed to the output layer.
In a perceptron, the input layer receives the input signals or data. This is then passed to the hidden layer(s), which is where the main computation and processing occurs. The hidden layers contain the algorithms that operate on the input signals, performing calculations and transformations to extract relevant features and patterns.
The processed data from the hidden layers is then passed to the output layer, which produces the final output or prediction of the perceptron based on the computations performed in the hidden layers.
The other options are incorrect:
A) The input layer simply passes the input data to the hidden layers for processing. It does not contain the main algorithms.
C) “Neurotransmitter” is not a component of a perceptron. In a biological neural network, a neurotransmitter is a chemical that transmits signals between neurons, but this is not a relevant term for artificial neural networks.
D) “Nucleus” is also not a component of a perceptron. In a biological cell, the nucleus contains genetic material, but artificial neurons do not have nuclei.
So in summary, the algorithms that process the input signals before passing the results to the output layer are located in the hidden layers of a perceptron. The hidden layers perform the key computations that enable the perceptron to learn and make predictions based on the input data.
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