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Questions and Answers
What is the main purpose of convolutional neural networks (CNNs)?
What is the main purpose of convolutional neural networks (CNNs)?
In the context of CNNs, what does LeNet-5 refer to?
In the context of CNNs, what does LeNet-5 refer to?
What is a key function of the pooling layers in CNNs?
What is a key function of the pooling layers in CNNs?
Which layer type helps in feature extraction in a Convolutional Neural Network?
Which layer type helps in feature extraction in a Convolutional Neural Network?
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What occurs during the flattening layer in a CNN?
What occurs during the flattening layer in a CNN?
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Which type of layer helps connect the output from convolutional layers to the fully connected layers in a CNN?
Which type of layer helps connect the output from convolutional layers to the fully connected layers in a CNN?
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What is the primary goal of a fully connected network in a CNN?
What is the primary goal of a fully connected network in a CNN?
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What is a distinct characteristic of Convolutional Neural Networks compared to traditional neural networks?
What is a distinct characteristic of Convolutional Neural Networks compared to traditional neural networks?
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What is the role of activation functions in Convolutional Neural Networks?
What is the role of activation functions in Convolutional Neural Networks?
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What is typically the last operation in a Convolutional Neural Network before making a prediction?
What is typically the last operation in a Convolutional Neural Network before making a prediction?
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Study Notes
Subsampling and Max Pooling
- Subsampling pixels reduces the number of parameters to characterize an image.
- Max pooling operates over each activation map independently with 2x2 filters and stride 2.
Pooling Layer
- Common settings for pooling layers: F = 2, S = 2 or F = 3, S = 2.
- Pooling layer reduces spatial dimensions to reduce number of parameters and computation.
Flattening and Fully Connected Network (FC)
- Flattening layer reshapes the output of convolutional and pooling layers into a 1D feature vector.
- Fully connected network (FC) is a feedforward neural network that connects every input to every output.
CNN: Extraction and Classification
- CNNs are used for object recognition, image classification, object detection, and face recognition.
- CNNs consist of feature extraction and classification stages.
LeNet-5
- LeNet-5 is a classic CNN architecture proposed by Yann LeCun et al. in the 1990s.
- LeNet-5 is used for handwritten and machine-printed character recognition.
Convolutional Layer
- A convolutional layer consists of a number of filters that convolve the input image.
- Each filter extends the full depth of the input volume.
- Convolutional operation involves element-wise multiplication and sum of a filter and a small chunk of the image.
Convolution Operation
- Convolutional operation involves sliding a filter over the image spatially, computing dot products.
- An activation map (feature map) is a 2D sheet of neuron outputs, each connected to a small region in the input.
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Description
Learn about subsampling pixels in images to make them smaller and reduce parameters, as well as how max pooling operates over activation maps independently with filters and stride. Explore the concepts of feature maps, pooling layers, flattening layers, and fully connected networks in Convolutional Neural Networks.
