2. Deep Learning and Variants_Session 2_20240120 - Neural Networks for Classification Quiz
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Questions and Answers

What is the purpose of using logistic regression with the MTcars dataset?

  • To determine the power of a vehicle's engine based on its weight and transmission type
  • To predict the weight of a vehicle given its engine power and type of transmission
  • To estimate the probability of a car having manual transmission based on its engine power and weight (correct)
  • To classify vehicles as manual or automatic based on their engine power and weight

    • What is a limitation of a single perceptron model?

  • It cannot handle non-linear data effectively (correct)
  • It always outputs a positive value
  • It is limited to only two input features
  • It does not work well with small datasets

    • What does the region below the line in the perceptron model signify?

  • Inconclusive results
  • Positive output values
  • Negative output values (correct)
  • Data points that should be ignored

    • How does a single perceptron handle non-linearity in data?

    <p>By converting non-linear data into linear form</p> Signup and view all the answers

    What are neurons like sigmoid et al. used for in deep learning?

    <p>To represent activation functions in neural networks</p> Signup and view all the answers

    Based on biological inspiration, what would intuition suggest when dealing with non-linear data using a single perceptron model?

    <p>Use multiple layers of neurons like the human brain does</p> Signup and view all the answers

    What is a key characteristic of a one hidden layer network?

    <p>It can learn any function</p> Signup and view all the answers

    In Artificial Neural Networks, what do hyper-parameters refer to?

    <p>Parameters like activation functions and network structure</p> Signup and view all the answers

    What is the purpose of using the softmax activation function in the output layer of a neural network?

    <p>To convert raw scores into probabilities for multi-class classification</p> Signup and view all the answers

    Which type of neural network is specifically designed for problems with non-linear relationships between inputs and outputs?

    <p>Feed Forward Neural Network</p> Signup and view all the answers

    What is the primary role of the transformation matrix in neural networks?

    <p>It represents all the connections between neurons as a matrix</p> Signup and view all the answers

    What distinguishes Multi Layer Perceptrons from traditional perceptrons?

    <p>MLPs have multiple hidden layers, while perceptrons have only one</p> Signup and view all the answers

    Which function is often used for the activation of neurons in the output layer of a neural network for regression problems?

    <p>Linear</p> Signup and view all the answers

    What does the term 'hyper-parameters' typically refer to in the context of Artificial Neural Networks?

    <p>Network structure and activation functions</p> Signup and view all the answers

    In a neural network, which function is commonly used in the output layer for multi-class classification problems?

    <p>Softmax</p> Signup and view all the answers

    What is the primary role of a transformation matrix in a neural network?

    <p>Changing representation space</p> Signup and view all the answers

    Which of the following is NOT a hyper-parameter in an Artificial Neural Network?

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

    For what type of problem is the Sigmoid activation function particularly useful in a neural network?

    <p>Classification problems</p> Signup and view all the answers

    What distinguishes Multi Layer Perceptrons from traditional perceptrons?

    <p>Non-linearity handling capability</p> Signup and view all the answers

    In deep learning, what do parameters like 'Number of hidden layers' and 'Number of neurons' represent?

    <p>'Hyper-parameters'</p> Signup and view all the answers

    What is a key characteristic of a Multi Layer Perceptron (MLP)?

    <p>Consists of multiple hidden layers</p> Signup and view all the answers

    What type of activation function is often employed in the output layer for binary classification problems?

    <p>Sigmoid</p> Signup and view all the answers

    What model is used to estimate the probability of a vehicle being fitted with a manual transmission using the MTcars dataset?

    <p>Perceptron</p> Signup and view all the answers

    In logistic regression, what does a positive value output by a single perceptron represent?

    <p>Region above the line</p> Signup and view all the answers

    Why is a single perceptron not suitable for handling non-linear data?

    <p>It can't create a boundary beyond linear regions</p> Signup and view all the answers

    What is the limitation of a single perceptron when dealing with binary classification similar to the 'Will it work here?' example provided?

    <p>It cannot represent non-linear decision boundaries</p> Signup and view all the answers

    Based on biological inspiration, what approach would intuition suggest when dealing with non-linear data using artificial neurons?

    <p>Combine multiple layers of neurons</p> Signup and view all the answers

    What is the primary role of the sigmoid function in artificial neural networks?

    <p>Handle non-linear relationships</p> Signup and view all the answers

    In the given logistic regression formula, what does the term 'hp' represent?

    <p>Horsepower of the engine</p> Signup and view all the answers

    'Will it work here?' table demonstrates an issue faced by which type of model due to its linearity?

    <p>'Perceptron' model</p> Signup and view all the answers

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