Podcast
Questions and Answers
Adaline stands for Adaptive Linear ________
Adaline stands for Adaptive Linear ________
Neural
A unit with a linear activation function is called a ________ unit
A unit with a linear activation function is called a ________ unit
linear
In Adaline, there is only one output ________
In Adaline, there is only one output ________
unit
Weights between the input unit and output unit are ________
Weights between the input unit and output unit are ________
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Adaline consists of trainable ________
Adaline consists of trainable ________
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In Adaline, the output values are bipolar (+1, -1). Weights between the input unit and output unit are ________
In Adaline, the output values are bipolar (+1, -1). Weights between the input unit and output unit are ________
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The learning rule in Adaline is found to minimize the mean square error between activation and target ________
The learning rule in Adaline is found to minimize the mean square error between activation and target ________
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Adaline compares actual output with calculated output and updates the weight based on the error calculated by the ________ learning rule
Adaline compares actual output with calculated output and updates the weight based on the error calculated by the ________ learning rule
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In Adaline, all input neurons are directly connected to the output neuron with weighted connected ________
In Adaline, all input neurons are directly connected to the output neuron with weighted connected ________
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Adaline consists of adjustable ________
Adaline consists of adjustable ________
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There is a bias b of activation function 1 present in Adaline, which acts as a ________
There is a bias b of activation function 1 present in Adaline, which acts as a ________
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First, calculate the net input to your Adaline network, then apply the activation function to its output, and compare it with the original output. If not equal, send an error back to the network and update the weight according to the error calculated by the ________ rule
First, calculate the net input to your Adaline network, then apply the activation function to its output, and compare it with the original output. If not equal, send an error back to the network and update the weight according to the error calculated by the ________ rule
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