Convolutional Neural Networks (CNNs) are a specialized type of artificial neural network primarily used in the field of computer vision. CNNs are designed to automatically and adaptively learn spatial hierarchies of features from input images. This allows them to excel at tasks such as image classification, object detection, and pattern recognition.
CNNs are composed of several key layers, including:
Convolutional Layers: These layers apply a set of filters to the input image to extract features such as edges, textures, and patterns. Each filter produces a feature map, and multiple feature maps are combined to capture complex structures in the image.
Pooling Layers: Pooling layers reduce the spatial dimensions of the feature maps, making the network more computationally efficient and helping to make the detected features more robust to variations in scale and position.
Fully Connected Layers: After several convolutional and pooling layers, the output is typically flattened and passed through fully connected layers. These layers use the extracted features to make final predictions, such as classifying the input image.
Activation Functions: Non-linear activation functions like ReLU (Rectified Linear Unit) are used to introduce non-linearity into the model, enabling it to learn more complex patterns.
CNNs have revolutionized the field of computer vision and are widely used in applications ranging from autonomous vehicles and facial recognition to medical image analysis and beyond. Their ability to learn from data without requiring manual feature extraction makes them a powerful tool in modern AI.