Part 2: Hyperparameter Tuning and Basic Architectures

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Questions and Answers

In hyperparameter tuning, what is the purpose of a learning rate?

  • To control the number of epochs
  • To determine the size of each batch
  • To adjust the step size during optimization (correct)
  • To set the dropout rate

How does dropout contribute to regularization in a CNN?

  • By reducing the number of neurons in the network
  • By randomly dropping connections during training (correct)
  • By increasing the learning rate
  • By adjusting the kernel size

Which optimization algorithm is known for adapting the learning rate on a per-parameter basis?

  • Stochastic Gradient Descent (SGD)
  • Adaptive Moment Estimation (Adam) (correct)
  • Root Mean Square Propagation (RMSprop)
  • Mini-batch Gradient Descent

What is the primary role of the validation set in hyperparameter tuning?

<p>To evaluate the model on unseen data (C)</p> Signup and view all the answers

How does transfer learning benefit CNNs in practice?

<p>By initializing the network with pre-trained weights (B)</p> Signup and view all the answers

What is the primary challenge addressed by techniques like L2 regularization in CNNs?

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

In a CNN, what does the term "receptive field" refer to?

<p>The area of the input that a neuron in a particular layer sees (A)</p> Signup and view all the answers

How does a CNN handle input data of varying sizes?

<p>By resizing all input data to a fixed size (A)</p> Signup and view all the answers

Why do CNNs exhibit translation invariance?

<p>Due to parameter sharing in convolutional layers (A)</p> Signup and view all the answers

Which layer type is responsible for introducing non-linear transformations to the input in a CNN?

<p>Activation Layer (C)</p> Signup and view all the answers

Flashcards

Learning Rate in CNNs

The step size used during optimization to adjust weights based on the gradient.

Dropout Regularization

A technique that randomly disables a percentage of connections during training to prevent overfitting.

Adam Optimizer

An optimization algorithm that adjusts the learning rate for each parameter individually based on its historical gradients.

Validation Set

A dataset used to evaluate the performance of a trained model on unseen data, crucial for hyperparameter tuning.

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Transfer Learning in CNNs

The practice of using pre-trained weights from a model trained on a large dataset to initialize a new model for a similar task.

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Overfitting in CNNs

Overfitting is a phenomenon where a model performs well on the training data but poorly on unseen data.

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Receptive Field

The area of the input image that a neuron in a convolutional layer sees.

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Handling Varying Image Sizes

CNNs handle different image sizes by pre-processing them to a fixed size, ensuring uniform input for the network.

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Translation Invariance

The property of CNNs to recognize the same object regardless of its position in the input image.

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Activation Layers

Activation layers like ReLU introduce non-linearity in a CNN, enabling the model to learn complex patterns.

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