Part 1: Fundamentals of Neural Networks and CNN Basics

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Questions and Answers

What does ANN stand for?

  • Artificial Numerical Network
  • Advanced Neural Network
  • Artificial Neural Network (correct)
  • Automated Neural Network

Which function is commonly used as an activation function in neural networks for introducing non-linearity?

  • Linear
  • Sigmoid
  • Tanh
  • Both B and C (correct)

What is the primary purpose of pooling layers in a CNN?

  • Increasing model complexity
  • Reducing spatial dimensions (correct)
  • Adding non-linearity
  • Controlling overfitting

In a CNN, what does the term "stride" refer to in the context of convolutional layers?

<p>The amount of overlap between neighboring regions (B)</p> Signup and view all the answers

What is the primary role of the activation layer in a CNN?

<p>Applying non-linear transformations to the input (A)</p> Signup and view all the answers

Why do CNNs use parameter sharing in convolutional layers?

<p>To make the network translation invariant (C)</p> Signup and view all the answers

What is the primary advantage of CNNs over fully connected networks?

<p>Parameter sharing (A)</p> Signup and view all the answers

What is the purpose of dropout layers in a CNN?

<p>To prevent overfitting (B)</p> Signup and view all the answers

Which layer type is responsible for reducing the size of feature maps through downsampling in a CNN?

<p>Pooling Layer (B)</p> Signup and view all the answers

What is the primary benefit of using an activation function like ReLU in CNNs?

<p>It introduces non-linearity (B)</p> Signup and view all the answers

Flashcards

What does ANN stand for?

A type of neural network inspired by the structure and function of the human brain.

What is an activation function?

A function that introduces non-linearity in neural networks, allowing them to learn complex relationships.

What is the purpose of pooling layers in a CNN?

To reduce the spatial dimensions of the feature maps, leading to a smaller and more manageable representation.

What is the stride in a convolutional layer?

The number of pixels the convolutional filter moves across the input image in each step.

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What is the role of the activation layer in a CNN?

To apply non-linear transformations to the input data, allowing the network to learn complex patterns.

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Why do CNNs use parameter sharing?

The same set of weights is used across the entire input image, allowing the network to recognize patterns regardless of their location.

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What is the advantage of CNNs over fully connected networks?

CNNs are better at handling spatial information and recognizing patterns in images, while fully connected networks are more general purpose.

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What is the purpose of dropout layers in a CNN?

To prevent overfitting by randomly dropping units during training, which helps the network generalize better.

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What is the role of pooling layers in a CNN?

The primary function of pooling layers is to downsample the feature maps, reducing their spatial dimensions.

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What is the benefit of using ReLU activation function?

ReLU introduces non-linearity, preventing the network from behaving like a linear function and enabling it to learn complex relationships.

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