What are Neural Networks?
Neural networks are a subset of machine learning models inspired by the human brain's architecture. They consist of interconnected nodes, also known as neurons, organized in layers: an input layer, one or more hidden layers, and an output layer. The primary function of a neural network is to recognize patterns within data and make predictions based on that information.
Each neuron receives input from multiple other neurons, processes it through an activation function, and transmits the output to neurons in subsequent layers. The learning process involves adjusting the weights of connections based on the error of the predicted output compared to the actual target value, typically utilizing a method called backpropagation.
Neural networks excel at various tasks, including image and speech recognition, natural language processing, and game playing. Their ability to learn from vast amounts of data makes them particularly powerful in today's data-driven world. Additionally, advancements such as deep learning, which uses deep neural networks with many layers, have further enhanced their capabilities, allowing them to tackle complex problems with high accuracy.
In summary, neural networks are a crucial technology in the field of artificial intelligence, enabling machines to learn from experience and improving their performance over time.