CNNs are a type of deep learning model widely used in image recognition and computer vision tasks. They excel at automatically learning spatial hierarchies from pixel data, making them ideal for processing visual information.

🔍 Key Concepts of CNNs

  • Convolutional Layers: Apply filters to detect features like edges or textures
    Convolutional_Layer
  • Pooling Layers: Reduce spatial dimensions while retaining important features
    Pooling_Layer
  • Fully Connected Layers: Classify features into final output categories
    Fully_Connected_Layer
  • Activation Functions: Introduce non-linearity (e.g., ReLU, Tanh)
    ReLU_Activation_Function

🧠 How CNNs Work

  1. Input Layer receives raw pixel data
  2. Convolutional Layers extract local features through filters
  3. Pooling Layers downsample data to reduce complexity
  4. Flattening converts 2D feature maps to 1D arrays
  5. Fully Connected Layers perform classification using learned weights

For deeper insights into CNN architecture, visit our CNN Architecture Guide.