Convolutional Neural Networks (CNNs) are a class of deep learning algorithms primarily used for image processing and computer vision tasks. They mimic the visual perception of humans by leveraging convolutional layers to automatically detect spatial hierarchies in data.

Key Components of CNNs

  1. Convolutional Layers

    • Apply filters (kernels) to extract features like edges, textures, and patterns.
    • Use ReLU activation to introduce non-linearity.
    • 📌 Example: CNN Architecture Diagram

  2. Pooling Layers

    • Reduce spatial dimensions (e.g., max pooling for downsampling).
    • Preserve important features while minimizing computation.

  3. Fully Connected Layers

    • Final layers that classify features into specific categories.
    • Often used after convolution and pooling operations.

4️⃣ Applications Include:

  • Image classification 📷
  • Object detection 🔍
  • Facial recognition 👀
  • Medical imaging 🩺

Advantages of CNN vs. Traditional Methods

  • Automatic Feature Extraction: Reduces manual feature engineering.
  • Translation Invariance: Detects patterns regardless of their position in the input.
  • Efficient Computation: Pooling layers reduce data size.

📚 Further Reading

Convolutional Neural Network Structure

For visual learners, this interactive demo provides a hands-on overview of CNN operations.