Abstract

Neural networks are a foundational component of modern artificial intelligence (AI), inspired by the structure and functioning of the human brain. These computational models consist of layers of interconnected nodes, or “neurons,” that process data by learning patterns and relationships from input information. Neural networks enable machines to perform tasks such as classification, prediction, and pattern recognition, often achieving results that surpass traditional algorithms. Over the past decades, neural networks have evolved from simple perceptrons to deep architectures containing many hidden layers, known as deep neural networks (DNNs). Specialized forms such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have expanded the scope of neural networks to handle images, video, speech, and sequential data effectively. These models are the backbone of many advanced applications, including facial recognition, autonomous driving, medical image diagnosis, natural language processing, and recommendation systems. Training neural networks involves adjusting the weights of connections between neurons using algorithms like backpropagation and optimization methods such as stochastic gradient descent. While powerful, neural networks also face challenges such as the need for large datasets, high computational demands, and issues related to explainability and overfitting. Recent research is focused on improving neural network efficiency, interpretability, and generalization through methods like transfer learning, pruning, and explainable AI (XAI). As neural networks continue to advance, they remain at the heart of innovations driving intelligent systems across industries.