Neural networks, also known as artificial neural networks (ANNs), are a key component of artificial intelligence (AI). Modelled after the human brain, they’re designed to replicate the way humans learn by processing interconnected layers of artificial neurons, also known as nodes. Each node simulates a neuron and is responsible for processing information by performing computations and applying activation functions on the input data.
A neural network functions through a series of connected layers, typically consisting of input, hidden, and output layers. The input layer receives raw data, the hidden layers perform computations and transformations, and the output layer provides the final result. Each layer consists of multiple nodes that contain weights and bias terms, which adjusts as the model “learns” from the training data. The network learns by iteratively making predictions on the data, comparing the predictions to the true values, and adjusting the weights and biases to minimize the error in the predictions.
Neural networks are widely used in various applications such as image recognition, voice recognition, natural language processing, and recommender systems. They are behind the technology that allows voice assistants like Siri or Alexa to interpret spoken language, self-driving cars to recognize objects, and Google to provide search recommendations. Despite their computational complexity and the vast amount of data needed for optimal training, neural networks have proven incredibly effective at extracting patterns and making predictions, making them a cornerstone of modern AI systems.