Neural Network

Neural networks are the foundational building blocks of modern AI, consisting of layers of artificial neurons (nodes) connected by weighted edges. Each neuron receives inputs, applies a mathematical transformation, and passes the result to the next layer. Through training on large datasets, the network adjusts its weights to minimize prediction errors, effectively learning to recognize patterns and make decisions.

A basic neural network has three types of layers: an input layer that receives data, one or more hidden layers that process information, and an output layer that produces results. Deep neural networks have many hidden layers, which is where the term "deep learning" originates. Different architectures like convolutional neural networks (CNNs) for images and recurrent neural networks (RNNs) for sequences are optimized for specific data types.

Neural networks learn through a process called backpropagation, where errors in predictions are propagated backward through the network to update weights. Combined with gradient descent optimization, this allows neural networks to gradually improve their performance on tasks ranging from image recognition to language generation. The transformer architecture used in modern LLMs is a specialized type of neural network.