Foundations
Neural network
A neural network is a computational model inspired by the structure and function of the human brain. It consists of interconnected nodes (neurons) organized in layers that process and transmit information to solve complex tasks like pattern recognition, classification, and prediction.
Explanation
Neural networks are composed of interconnected nodes (neurons) arranged in input, hidden, and output layers. Each connection between neurons has a weight associated with it, representing the strength of the connection. During processing, input data is fed into the input layer, and each neuron applies a mathematical function (activation function) to its weighted inputs, passing the result to the next layer. The network learns by adjusting these weights through a process called training, where it is exposed to labeled data and iteratively refines the weights to minimize the difference between its predictions and the actual values. Different architectures, such as convolutional neural networks (CNNs) for image processing and recurrent neural networks (RNNs) for sequential data, are designed to address specific types of problems. The ability of neural networks to learn complex, non-linear relationships from data has made them a cornerstone of modern AI.