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Questions and Answers
What is the purpose of the activation function in a convolutional neural network?
What is the purpose of the activation function in a convolutional neural network?
- To reduce the dimensions of the feature maps
- To perform convolution operations
- To introduce non-linearity into the network (correct)
- To flatten the feature maps
What is the main purpose of the pooling layer in a convolutional neural network?
What is the main purpose of the pooling layer in a convolutional neural network?
- To extract features from the input data
- To perform convolution operations
- To reduce the dimensions of the feature maps (correct)
- To introduce non-linearity into the network
What is the purpose of the flatten layer in a convolutional neural network?
What is the purpose of the flatten layer in a convolutional neural network?
- To introduce non-linearity into the network
- To enable the network to process the extracted features (correct)
- To perform convolution operations
- To reduce the dimensions of the feature maps
What is the output layer responsible for in a convolutional neural network?
What is the output layer responsible for in a convolutional neural network?
Why is it not preferred to use a large number of fully connected layers?
Why is it not preferred to use a large number of fully connected layers?
What is the purpose of the convolutional layer in a convolutional neural network?
What is the purpose of the convolutional layer in a convolutional neural network?
What is the advantage of CNNs over FC networks when dealing with images?
What is the advantage of CNNs over FC networks when dealing with images?
What is the purpose of the convolutional layer in a CNN?
What is the purpose of the convolutional layer in a CNN?
What is the purpose of max pooling in a convolutional neural network?
What is the purpose of max pooling in a convolutional neural network?
What is the purpose of the hidden layers in a convolutional neural network?
What is the purpose of the hidden layers in a convolutional neural network?
How many filters are typically applied to the input data in a convolutional layer?
How many filters are typically applied to the input data in a convolutional layer?
What is the advantage of CNNs in terms of the number of trainable parameters?
What is the advantage of CNNs in terms of the number of trainable parameters?
What is the role of the input layer in a CNN?
What is the role of the input layer in a CNN?
What can the input layer in a CNN accept?
What can the input layer in a CNN accept?
What is the number of trainable parameters in a filter with size 3x3 and 4 channels?
What is the number of trainable parameters in a filter with size 3x3 and 4 channels?
What is the purpose of a filter in a convolutional layer?
What is the purpose of a filter in a convolutional layer?
What is the resulting image after applying a filter to an input image?
What is the resulting image after applying a filter to an input image?
What is the mathematical equation of a filter in a convolutional layer?
What is the mathematical equation of a filter in a convolutional layer?
Why do we need multiple filters in a convolutional layer?
Why do we need multiple filters in a convolutional layer?
What is the relationship between the number of channels in the input image and the number of channels in the filter?
What is the relationship between the number of channels in the input image and the number of channels in the filter?
What is the primary function of a convolutional layer?
What is the primary function of a convolutional layer?
What is the characteristic of the convolution operation in a convolutional layer?
What is the characteristic of the convolution operation in a convolutional layer?
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Study Notes
CNN vs. Fully-Connected Network
- CNNs are better than FCNs when dealing with images because they preserve the dimensional relationships between image pixels, whereas FCNs rearrange them, resulting in the loss of this dimensional relationship.
- CNNs have a smaller number of parameters to train, which means faster and more accurate training.
CNN Construction
- The input layer receives raw pixel values of the input image and passes them forward to subsequent layers for feature extraction and classification.
- Convolutional layers apply specified filters to the input data to extract relevant features, with each filter searching for one feature in the image.
- Activation functions are applied after convolution to add non-linearity to the network.
- Pooling layers (max or avg) reduce the dimensions of feature maps obtained from convolutional layers, extracting the most important information.
- Flatten layers serve as a bridge between convolutional and fully connected layers, enabling the network to process extracted features and make predictions.
Convolutional Layers
- Each filter extracts one feature from the image, and the network determines the feature during training.
- Each filter produces one feature map, and applying multiple filters detects multiple features from the image.
- The number of trainable parameters in a layer is calculated by counting the number of filter parameters.
Convolutional Operation
- The mathematical equation of a filter is Y = w * X + b, making the convolution operation a linear function.
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