Convolutional Neural Networks (CNNs) are a specialized type of artificial neural network that has become the gold standard for processing and analyzing visual data. Their architecture is uniquely designed to take advantage of the spatial structure of images, enabling them to excel in tasks such as image classification, object detection, and facial

Feedforward neural networks (FNNs) are among the simplest and most fundamental types of artificial neural networks, serving as the foundation for more complex architectures in deep learning. These networks are designed to model complex relationships within data, making them widely applicable for various tasks, including classification, regression,

Neural networks have become a cornerstone of artificial intelligence and machine learning, enabling machines to learn from data and make informed decisions. Within the broad category of neural networks, several specialized architectures exist, each designed to address specific tasks and types of data. Understanding these different types is essentia

Neural networks are a fundamental component of deep learning, serving as the architecture that enables machines to learn from vast amounts of data. Inspired by the biological neural networks in the human brain, artificial neural networks consist of interconnected nodes, or "neurons," which work together to process information and recognize patterns

Deep learning is a subset of artificial intelligence (AI) and machine learning that focuses on using neural networks with many layers to analyze various forms of data. It is inspired by the structure and function of the human brain, specifically the way neurons interact to process information. Deep learning has gained significant attention in recen