Convolutional Neural Networks (CNNs) are a class of deep learning models designed to process spatial data like images. They excel at tasks such as image classification, object detection, and pattern recognition due to their ability to automatically learn spatial hierarchies from input.
Key Components of CNNs
Convolutional Layers
- Apply filters (kernels) to detect features like edges, textures, and shapes.
- 📎 Learn more about filters
Pooling Layers
- Reduce spatial dimensions while retaining important features (e.g., max pooling).
- 📎 Explore pooling techniques
Fully Connected Layers
- Final layers that classify the extracted features into meaningful outputs.
Activation Functions
- Commonly use ReLU to introduce non-linearity.
Applications of CNNs
- 🖼️ Image Recognition: Detect objects in photos (e.g., Image_Recognition).
- 📊 Medical Imaging: Analyze X-rays, MRIs, and histopathology slides.
- 📷 Autonomous Vehicles: Identify pedestrians, traffic signs, and lanes.
Why Use CNNs?
- 🧠 Hierarchical Feature Learning: Automatically capture patterns from raw data.
- 🔄 Translation Invariance: Detect features regardless of their position in the input.
Further Learning
For a deeper dive into CNN architectures, check out our Deep Learning Fundamentals guide. Want to see visual examples? Explore Convolutional_Neural_Network for diagrams.