Weight Adjustment in Neural Networks
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Questions and Answers

Which of the following is the primary purpose of the backpropagation algorithm in a multilayer perceptron?

  • To determine the optimal number of hidden layers
  • To initialize the weights of the connections between neurons
  • To update the weights of the connections between neurons in the hidden layers (correct)
  • To calculate the error between the actual and desired output
  • Which of the following activation functions is most commonly used in the hidden layers of a multilayer perceptron?

  • Softmax function
  • Rectified Linear Unit (ReLU) (correct)
  • Tanh function
  • Sigmoid function
  • What is the purpose of tuning the hyperparameters of a multilayer perceptron?

  • To improve the accuracy of the model on the training data
  • To determine the optimal learning rate for the backpropagation algorithm
  • To optimize the performance of the model on the validation or test data (correct)
  • To increase the number of hidden layers in the network
  • What is the primary difference between a single-layer perceptron and a multilayer perceptron?

    <p>The ability to solve non-linear problems</p> Signup and view all the answers

    Which of the following is a key hyperparameter that can be tuned to improve the performance of a multilayer perceptron?

    <p>The learning rate</p> Signup and view all the answers

    What is the purpose of the input layer in a multilayer perceptron?

    <p>To distribute the input data to the neurons in the hidden layers</p> Signup and view all the answers

    What is a key difference between Adaline and the standard perceptron in the learning phase?

    <p>In Adaline, weights are adjusted according to the weighted sum of the inputs, while in the standard perceptron, the net is passed to the activation function for weight adjustment.</p> Signup and view all the answers

    What type of activation function does Adaline use?

    <p>Bipolar activation function</p> Signup and view all the answers

    Which training algorithm does Adaline employ to minimize Mean-Squared Error during training?

    <p>Delta rule</p> Signup and view all the answers

    What is adjusted during the training of an Adaline network?

    <p>Weights and bias</p> Signup and view all the answers

    How does the architecture of Adaline differ from a standard perceptron?

    <p>Adaline has an extra feedback loop which compares actual output with desired output, unlike a standard perceptron.</p> Signup and view all the answers

    What is initialized at the start of training in an Adaline network?

    <p>Weights, Bias, Learning rate</p> Signup and view all the answers

    What is the purpose of adding the 'bias weight' in the perceptron algorithm?

    <p>To shift the activation function left or right on the number graph</p> Signup and view all the answers

    How does the activation function in a multilayer perceptron differ from the classic perceptron?

    <p>The multilayer perceptron uses a variety of real-valued activation functions, while the classic perceptron uses a boolean step function</p> Signup and view all the answers

    What is the role of backpropagation in training a multilayer perceptron?

    <p>Backpropagation is used to compute the gradients and update the weights of the multilayer perceptron</p> Signup and view all the answers

    Which of the following is NOT a hyperparameter that can be tuned in a multilayer perceptron?

    <p>The number of hidden layers</p> Signup and view all the answers

    How does the architecture of a multilayer perceptron differ from the classic single-layer perceptron?

    <p>The multilayer perceptron has multiple hidden layers, while the classic perceptron has a single output layer</p> Signup and view all the answers

    What is the purpose of regularization techniques in training a multilayer perceptron?

    <p>Regularization is used to prevent overfitting by constraining the model complexity</p> Signup and view all the answers

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