developer-roadmap/roadmaps/machine-learning/content/convolutional-neural-network@BtO2wH7YYqE25HShI6sd9.md

Convolutional Neural Networks

Convolutional Neural Networks (CNNs) are a specialized type of artificial neural network primarily used for processing data that has a grid-like topology, such as images. They employ convolutional layers to automatically and adaptively learn spatial hierarchies of features from the input data. These layers use filters (or kernels) that slide across the input, performing element-wise multiplication and summation to detect patterns. Pooling layers are often used to reduce the spatial dimensions of the feature maps, decreasing computational complexity and making the network more robust to variations in the input.

Visit the following resources to learn more:

• @article@What are Convolutional Neural Networks?
• @article@An Introduction to Convolutional Neural Networks (CNNs)
• @video@Hot Dog or Not Hot Dog – Convolutional Neural Network Course for Beginners