What are Neural Networks?
Neural networks are a subset of machine learning models inspired by the human brain's architecture. They consist of interconnected nodes or neurons organized in layers: input, hidden, and output layers. Each connection has an associated weight that adjusts as learning proceeds, enabling the network to make predictions based on input data. In computer vision, neural networks are particularly powerful for tasks such as image classification, object detection, and image generation.
Convolutional Neural Networks (CNNs) are a specialized type of neural network designed explicitly for processing structured grid data like images. CNNs utilize convolutional layers to automatically and adaptively learn spatial hierarchies of features, making them highly effective for visual tasks. Training a neural network requires a large set of labeled data and usually involves backpropagation, an optimization technique that minimizes the error of predictions by adjusting weights based on the derivative of the error function.
In conclusion, neural networks are critical to advancements in artificial intelligence, particularly in computer vision, driving innovations across various fields such as healthcare, autonomous vehicles, and security systems.