💡 What are CNNs?
Convolutional Neural Networks (CNNs) are a class of deep learning algorithms primarily used for processing grid-like data (e.g., images, videos). They mimic the human visual system by applying filters to detect patterns such as edges, textures, and objects.

Key Components of CNNs

  1. Convolutional Layers

    • Apply filters (kernels) to input data to extract features.
    • Example: Detecting edges in an image using a 3x3 kernel.
    Convolutional Layer

  2. Pooling Layers

    • Reduce spatial dimensions (e.g., Max Pooling, Average Pooling).
    • Helps in decreasing computational complexity and overfitting.

  3. Fully Connected Layers

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

  4. Activation Functions

    • Commonly use ReLU (Rectified Linear Unit) for non-linear transformations.

Applications of CNNs

  • 📸 Image Recognition
    • Object detection, facial recognition, and image classification.
    Image Recognition
  • 🎬 Video Analysis
    • Action recognition and scene understanding.
  • 📊 Medical Imaging
    • Diagnosing diseases from X-rays or MRI scans.

Learn More

If you're interested in diving deeper into CNNs, check out our Deep Learning Fundamentals tutorial for a broader overview. For hands-on practice, try building a CNN using TensorFlow or PyTorch.

🎯 Key Takeaway: CNNs excel at spatial hierarchies, making them ideal for tasks involving 2D or 3D data. Always experiment with different architectures to optimize performance!