Operations in Convolutional Neural Networks | Convolution, Pooling and Fully Connected Layer
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Operations in Convolutional Neural Networks | Convolution, Pooling and Fully Connected Layer
Learn about the steps involved in CNNs after an image is transformed into a pixel matrix. The pixel matrix goes through convolutional layer which extracts detailed features, then an activation functions is applied on it to convert the matrix into smaller matrix with important features. Then the fully connected layer is a neural network which ...
87.6K viewsOct 24, 2023
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Convolutions are fundamental operations in deep learning, especially within Convolutional Neural Networks (CNNs). They work by applying a small matrix, known as a kernel (or filter), that slides across an input image to detect key features like edges, textures, or shapes. At each step, the kernel multiplies its values with the pixels underneath and sums them up, creating a feature map that emphasizes certain patterns. For instance, in the MNIST dataset, a 3×3 kernel can scan a 28×28 grayscale im
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