Introduction to Convolutional Neural Networks (CNNs)

Convolutional neural networks (CNNs) are a type of artificial intelligence model that excel in image processing and computer vision tasks. Unlike simple neural networks, CNNs are designed to handle varying aspects of an image, including colors and backgrounds across multiple environments.

The key concepts behind CNNs include convolution, pooling, and fully-connected layers. Convolution involves sliding small matrices (kernels or filters) over the input data, calculating dot products, and deducing characteristics of the image. Pooling compresses the convoluted image, reducing noise and enhancing features. Fully-connected layers process the output of previous layers, allowing for classification and fitting non-linear data.

While CNNs can be computationally intensive, they offer significant benefits in handling complex images with varying backgrounds and colors. By understanding these concepts and techniques, developers can leverage CNNs to solve real-world image processing challenges.

Source: https://dev.to/samder/introduction-to-convolutional-neural-networks-4lmp