Convolutional Neural Networks (CNNs) are a class of deep neural networks that have been successfully applied to various image processing tasks. They are particularly effective for tasks such as image classification, object detection, and image segmentation.
Key Concepts
- Convolutional Layers: These layers apply various filters to the input image to extract features such as edges, textures, and shapes.
- Pooling Layers: These layers reduce the spatial dimensions of the feature maps, which helps to decrease the computational complexity and memory usage.
- Fully Connected Layers: These layers connect every neuron in the previous layer to every neuron in the current layer, allowing the network to learn complex patterns.
Applications
CNNs have been widely used in various fields, including:
- Image Classification: Classifying images into predefined categories, such as animals, plants, and vehicles.
- Object Detection: Detecting and localizing objects within an image.
- Image Segmentation: Assigning a semantic label to each pixel in an image.
Resources
For further reading, you can check out the following resources:
- Deep Learning with Python by François Chollet
- Convolutional Neural Networks for Visual Recognition by Andrej Karpathy
Convolutional Neural Network Architecture