Convolutional Neural Networks

CNNs are a class of deep neural networks designed for processing and analyzing visual data, particularly images and videos. CNNs have had a profound impact on computer vision and image analysis, leading to significant advancements in tasks such as image classification, object detection, facial recognition, image segmentation.

  1. Convolutional Layers: CNNs use convolutional layers to extract features from input images. These layers apply a set of learnable filters to the input image. Each filter detects specific patterns or features in the image, such as edges, corners, or textures.
  2. Pooling Layers: Pooling layers downsample the feature maps produced by convolutional layers. Max pooling and average pooling are common techniques used to reduce the spatial dimensions of the feature maps while retaining the most important information.
  3. Fully Connected Layers: After the convolutional and pooling layers, CNNs typically have one or more fully connected layers for classification or regression tasks. These layers learn to combine the extracted features for final predictions.
  4. Activation Functions: CNNs use activation functions like ReLU -Rectified Linear Unit introduces non-linearity into the model, allowing it to capture complex patterns in the data.
  5. Convolutional Filters: The convolutional filters are trained to recognize various low- to high-level features in images. In deeper layers, they can identify more complex patterns and objects.
  6. Feature Hierarchies: CNNs learn hierarchies of features, starting with simple features at lower layers and progressing to complex object representations at higher layers. This hierarchical feature learning is a key to their success in image analysis.

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