Convolutional Networks (ConvNets) are a type of neural network used for image and signal processing.

They are a key building block of Deep Learning models, and are particularly well-suited to image recognition tasks.

Convolutional Networks have a number of key features that make them useful in this domain:

• Local Receptive Fields: ConvNets use small, localized receptive fields to extract features from images.
• Weighted Averages: ConvNets use weighted averages to combine features from different parts of the image.
• Non-linearity: ConvNets are non-linear, allowing for complex feature extraction.

Convolutional Networks can be used for a variety of tasks, including but not limited to:

• Image Classification
• Image Segmentation
• Object Detection
• Image Generation

Convolutional Networks are often used in combination with other Deep Learning models to form a complete Deep Learning pipeline.

Want to know more about Convolutional Network Anatomy? Click here.

Want to know more about Convolutional Networks in Practice? Click here.