CNN is a type of deep learning model specifically designed for processing structured grid data, such as images. They are widely used for various tasks such as image classification, object detection, segmentation, and more. It consists of
Input Layer - It receives the raw image data as input. It consists of a grid of pixel values representing the intensity.
Convolutional Layers - It apply convolution operations to the input image. These operations involve sliding a small filter (also known as a kernel) over the input image and computing dot products to produce feature maps. Each filter detects specific patterns or features within the input image, such as edges, textures, or shapes
Activation Function - Typically, a non-linear activation function like ReLU (Rectified Linear Unit) is applied element-wise to each feature map after convolution.
Pooling Layers - It downsample the feature maps obtained from convolutional layers by reducing their spatial dimensions. Common pooling operations include max pooling and average pooling, which extract the maximum or average value within a local neighborhood, respectively.
Fully Connected Layers - It processes the flattened feature maps from the last convolutional. These layers perform a linear transformation followed by a non-linear activation function, enabling the network to learn complex relationships between features.
Output Layer - It produces the final predictions or outputs of the network. The number of nodes in the output layer depends on the specific task. For instance, in image classification, each node may correspond to a class label, and the output represents the predicted probabilities for each class.
Loss Function - It computes the difference between the predicted outputs and the ground truth labels or targets.
Optimization Algorithm - It updates the weights of the network to minimize the loss function during training.
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