Introduction to CNNs
Convolutional Neural Networks (CNNs) are a class of deep learning models designed to process data with grid-like structures, such as images. They excel at tasks like image classification, object detection, and pattern recognition due to their ability to automatically learn spatial hierarchies.
Key Concepts of CNNs
- Convolutional Layers: Apply filters to detect features (e.g., edges, textures) in input data.
- Pooling Layers: Reduce spatial dimensions (e.g., Max Pooling) to improve computational efficiency.
- Fully Connected Layers: Classify features into final output categories.
Applications of CNNs
- Image classification: Explore more
- Object detection: Learn about YOLO
- Medical imaging: Case study here
Why Use CNNs?
- Automatic Feature Extraction: Eliminates manual feature engineering.
- Translation Invariance: Detects patterns regardless of their position in the input.
- Scalability: Handles large datasets efficiently.