Podcast
Questions and Answers
Describe the basic building blocks of convolutional neural network
Describe the basic building blocks of convolutional neural network
learners
Contrast fully connected neural network with convolutional neural network
Contrast fully connected neural network with convolutional neural network
networks
Explain the rationale behind convolution, padding and pooling operations
Explain the rationale behind convolution, padding and pooling operations
reasons
Explain the reasons why convolutional neural ______ is efficient for image data
Explain the reasons why convolutional neural ______ is efficient for image data
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Convolutional ______: Input Feature recognition – Perform convolution operations to extract features from the input data. – Generate a feature map by sliding a filter over the input image and recognizing patterns.
Convolutional ______: Input Feature recognition – Perform convolution operations to extract features from the input data. – Generate a feature map by sliding a filter over the input image and recognizing patterns.
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Flashcards
Convolutional Neural Network (CNN) Building Blocks
Convolutional Neural Network (CNN) Building Blocks
CNNs use layers of filters (kernels) to extract features from images; these features are processed through convolution, padding, and pooling operations.
CNN vs. Fully Connected NN
CNN vs. Fully Connected NN
CNNs are more efficient for images due to their specialized layers to handle the spatial relationships of pixels. Fully connected layers process every single pixel in every layer.
Convolution Operation
Convolution Operation
A filter slides over input data, performing element-wise multiplication and summation to produce a feature map.
Padding
Padding
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Pooling
Pooling
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