Convolutional Neural Network (CNN)

Convolutional Neural Networks (CNNs) are a class of deep learning algorithms specifically designed for processing grid-like data, such as images. They are particularly effective in tasks where spatial hierarchies and patterns are important.

Key Components:

• Convolutional Layers: Apply convolution operations to detect features such as edges and textures. They use filters or kernels that slide over the input data.
• Activation Functions: Non-linear functions (e.g., ReLU) that introduce non-linearity into the model.
• Pooling Layers: Reduce the spatial dimensions of feature maps, helping to reduce computation and control overfitting.
• Fully Connected Layers: Classify the data based on extracted features.

Applications:

• Image Classification: Identifying objects or categories within an image (e.g., classifying images of animals).
• Object Detection: Locating objects within an image and classifying them (e.g., detecting faces in photos).
• Image Segmentation: Dividing an image into regions or segments for detailed analysis (e.g., medical imaging).
• Facial Recognition: Identifying or verifying individuals based on facial features.
• Video Analysis: Recognizing patterns or actions in video frames (e.g., surveillance).

CNNs are crucial for many modern AI applications due to their ability to learn and extract complex features from data.

Hope it helps !