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16. What is the sequence of steps followed in training a perceptron? 1. For a sample input, compute an output 2. Initialize weights of perceptron randomly 3. Go to the next batch of dataset 4. If the prediction does not match the output, change the weights
2,1,4,3
1,4,3,2
1,2,3,4
2,3,4,1
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17. The neural network given below takes two binary valued inputs x1, x12 ϵ {0, 1} and the activation function is the binary threshold function (h(x)=l if x>0;O otherwise). Which of the following logical functions does it compute?
OR
AND
NAND
None of these
18. What are the steps for using a gradient descent algorithm? 1. Calculate error between the actual value and the predicted value 2. Repeat until you find the best weights of network 3. Pass an input through the network and get values from output layer 4. Initialize random values for weight and bias 5. Go to each neurons which contributes to the error and change its respective values to reduce the error
4,3,1,5,2
1,2,3,4,5
3,4,5,2,1
2,3,4,5,1
19. A 4-input neuron has bias of 0 and weights 1, 2, 3 and 4. The transfer function is given by f(v)= max(O,v). The inputs are 4, 10, 5 and 20 respectively. The output will be
238
119
75
121
20. For the same size of training data as input, the fastest learning technique is
Supervised training with gradient descent error correction
Supervised training with stochastic method
Unsupervised training without error calculation
Unsupervised training with Hebbian method
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