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Questions and Answers
What is the primary purpose of interleaving between Convolution, ReLU, and max-pooling layers in a CNN?
What is the primary purpose of interleaving between Convolution, ReLU, and max-pooling layers in a CNN?
What activation function is commonly used after Convolution layers in a CNN?
What activation function is commonly used after Convolution layers in a CNN?
What is the purpose of padding in convolutional layers?
What is the purpose of padding in convolutional layers?
What happens to the size of the next layer when a 5x5 filter is applied to a 32x32 image?
What happens to the size of the next layer when a 5x5 filter is applied to a 32x32 image?
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What technique is used to reduce overfitting in CNN, especially in image processing domain?
What technique is used to reduce overfitting in CNN, especially in image processing domain?
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What happens to the image dimensions when rotating an image during data augmentation?
What happens to the image dimensions when rotating an image during data augmentation?
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What is the purpose of stride in convolutional layers?
What is the purpose of stride in convolutional layers?
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What type of patterns does a filter try to identify in a small rectangular region?
What type of patterns does a filter try to identify in a small rectangular region?
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What is the purpose of max-pooling in a CNN?
What is the purpose of max-pooling in a CNN?
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What is the effect of convolution operations on the original size of the image?
What is the effect of convolution operations on the original size of the image?
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What is the backbone of training in CNN?
What is the backbone of training in CNN?
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What type of features do filters detect in early layers?
What type of features do filters detect in early layers?
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What is the effect of larger strides on the spatial size of layers?
What is the effect of larger strides on the spatial size of layers?
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What is the primary advantage of using ReLU activation functions?
What is the primary advantage of using ReLU activation functions?
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What is the purpose of pooling operations in neural networks?
What is the purpose of pooling operations in neural networks?
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What is the primary difference between max-pooling and average pooling?
What is the primary difference between max-pooling and average pooling?
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How do the neurons in a fully connected layer connect to the previous layer?
How do the neurons in a fully connected layer connect to the previous layer?
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What is the primary difference between the operation of convolutional and pooling layers?
What is the primary difference between the operation of convolutional and pooling layers?
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Study Notes
Convolutional Neural Networks (CNNs)
- In most cases, multiple fully connected layers are used to increase computational power.
- Interleaving between layers: Convolution, pooling, and ReLU layers are alternated to increase network power.
- ReLU typically follows Convolution, and two or three sets of Convolution-ReLU combinations are followed by max-pooling.
LeNet-5 and Training of CNN
- LeNet-5 is another form of CNN.
- CNN training uses backpropagation (BP), a widely used method in traditional feed-forward neural networks.
- BP is used in up to 80% of neural network models.
Data Augmentation
- Data augmentation: generating new training examples by applying transformations to original examples.
- Data augmentation reduces overfitting in CNN, especially in image processing.
- Popular augmentation techniques include:
- Rotation: rotating images with or without preserving dimensions.
- Scaling: scaling images outward or inward.
Convolutional Layer
- Convolutional layer: a filter tries to identify a particular pattern in a small rectangular region.
- Multiple filters are required to capture all possible shapes.
- Filter operation: dot product is performed on corresponding elements in filters and local regions in the image.
Padding
- Convolution operations reduce the original image size, losing information along the border.
- Padding: adding pixels (set to zero) around the border of the feature map to maintain the spatial footprint.
Strides
- Stride: the distance covered by a step in convolution operations.
- It is common to use a stride of 1 (sometimes 2).
- Larger strides reduce the spatial size of layers, reducing storage required.
ReLU Layers
- ReLU typically follows the convolution layer.
- ReLU has the same form as in traditional neural networks.
- ReLU does not reduce the size of layers, as it is a one-to-one mapping of activation values.
Pooling
- Pooling operation: works on small regions in each layer, producing another layer with the same depth.
- Two types of pooling: max-pooling and average pooling.
- Max-pooling returns the maximum value in the local region, and is the more common type of pooling.
Fully Connected Layer
- Fully Connected Layer: a feed-forward neural network.
- It forms the last few layers in the network.
- Input to the fully connected layer is the output from the final pooling or convolutional layer.
- All neurons in a fully connected layer connect to all neurons in the previous layer.
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Description
Test your understanding of convolutional neural networks, including filter sizes, image processing, and pattern recognition. Learn how filters work in convolutional layers and more!