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Questions and Answers
In artificial neurons, the weights represent the importance of outputs.
In artificial neurons, the weights represent the importance of outputs.
False
The bias in an artificial neuron is fixed and does not change during training.
The bias in an artificial neuron is fixed and does not change during training.
False
The activation function combines the weighted inputs and outputs to determine the neuron's output signal.
The activation function combines the weighted inputs and outputs to determine the neuron's output signal.
False
A decision boundary is a physical line or surface that separates different outputs based on weights and bias.
A decision boundary is a physical line or surface that separates different outputs based on weights and bias.
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The update rule in artificial neurons is responsible for generating random weights.
The update rule in artificial neurons is responsible for generating random weights.
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In assigning weights based on importance, less important inputs receive higher weights.
In assigning weights based on importance, less important inputs receive higher weights.
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If the weighted sum is equal to the bias with a step function, the output is 1.
If the weighted sum is equal to the bias with a step function, the output is 1.
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Weights, biases, activation function, and decision boundary do not play a role in determining the neuron's output.
Weights, biases, activation function, and decision boundary do not play a role in determining the neuron's output.
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In artificial neurons, adjusting weights and biases has no impact on improving performance or learning from mistakes.
In artificial neurons, adjusting weights and biases has no impact on improving performance or learning from mistakes.
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An artificial neuron consists of inputs, weights, bias, and subtraction function.
An artificial neuron consists of inputs, weights, bias, and subtraction function.
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Weights in an artificial neuron determine the importance or strength of each input.
Weights in an artificial neuron determine the importance or strength of each input.
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The role of the bias in an artificial neuron is to introduce randomness into the output.
The role of the bias in an artificial neuron is to introduce randomness into the output.
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The activation function in an artificial neuron combines the inputs and weights to determine the output.
The activation function in an artificial neuron combines the inputs and weights to determine the output.
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Artificial neurons are the basic components of neural networks.
Artificial neurons are the basic components of neural networks.
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In an artificial neuron, the inputs represent the recipe for cooking.
In an artificial neuron, the inputs represent the recipe for cooking.
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An artificial neuron is like a chef in a kitchen because both combine ingredients based on a recipe.
An artificial neuron is like a chef in a kitchen because both combine ingredients based on a recipe.
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The special weight that acts as a threshold in an artificial neuron is called the weight.
The special weight that acts as a threshold in an artificial neuron is called the weight.
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The weighted inputs and bias in an artificial neuron are combined to determine the final output.
The weighted inputs and bias in an artificial neuron are combined to determine the final output.
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