Convolutional Neural Networks (CNNs) are a class of deep learning models designed to process grid-like data (e.g., images, videos). They excel at tasks like image recognition, object detection, and pattern analysis due to their local receptive fields and shared weights architecture. Here's a breakdown:
🧩 Key Components of CNNs
Convolutional Layer
- Applies filters (kernels) to detect features like edges, textures, or shapes.
- Example: A 3x3 filter scanning an image to identify horizontal lines.
Pooling Layer
- Reduces spatial dimensions (e.g., width/height) to lower computational load.
- Common types: Max Pooling (retains strongest signal), Average Pooling (averages values).
Fully Connected Layer
- Links all neurons from previous layers to classify the input.
- Final layer produces output probabilities (e.g., for image classification).
📚 How CNNs Work Step-by-Step
- Input Layer: Accepts raw pixel data of an image.
- Feature Extraction: Convolutional layers apply filters to identify patterns.
- Dimensionality Reduction: Pooling layers compress the feature maps.
- Classification: Fully connected layers make predictions based on extracted features.
For deeper insights into neural network fundamentals, visit our AI Tutorial.
Explore practical examples of CNN architectures here.