A Convolutional Neural Network (CNN) is a type of deep learning model specifically designed for processing visual data like images and videos.
CNNs automatically detect patterns, features, and spatial hierarchies in images, making them ideal for tasks like object detection and image classification.
How CNN Works
- Convolution Layer: Applies filters to input images to extract features like edges, colors, and textures.
- Pooling Layer: Reduces the size of feature maps, keeping important information and improving efficiency.
- Flattening: Converts pooled feature maps into a single vector.
- Fully Connected Layer: Processes the vector to make predictions or classifications.
- Output Layer: Produces the final class or value prediction.
Advantages of CNNs
- Automatically extracts features from raw images
- Handles high-dimensional visual data efficiently
- Reduces preprocessing compared to traditional methods
- High accuracy for image and video tasks
Disadvantages
- Requires large datasets for training
- Computationally intensive
- Less effective for non-visual data
Real-World Examples
- Facial recognition in security systems
- Autonomous vehicles detecting obstacles
- Medical imaging for detecting tumors
- Image search engines
- Video surveillance
Conclusion
CNNs are the backbone of modern computer vision. By learning visual patterns automatically, they enable AI to “see” and understand the world.
Citations
https://savanka.com/category/learn/ai-and-ml/
https://www.w3schools.com/ai/